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Reinforced-Learning-Godot/rl/Lib/site-packages/sympy/physics/control/lti.py
Kilokem 40e2a747cf I am done
2024-10-30 22:14:35 +01:00

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from typing import Type
from sympy import Interval, numer, Rational, solveset
from sympy.core.add import Add
from sympy.core.basic import Basic
from sympy.core.containers import Tuple
from sympy.core.evalf import EvalfMixin
from sympy.core.expr import Expr
from sympy.core.function import expand
from sympy.core.logic import fuzzy_and
from sympy.core.mul import Mul
from sympy.core.numbers import I, pi, oo
from sympy.core.power import Pow
from sympy.core.singleton import S
from sympy.core.symbol import Dummy, Symbol
from sympy.functions import Abs
from sympy.core.sympify import sympify, _sympify
from sympy.matrices import Matrix, ImmutableMatrix, ImmutableDenseMatrix, eye, ShapeError, zeros
from sympy.functions.elementary.exponential import (exp, log)
from sympy.matrices.expressions import MatMul, MatAdd
from sympy.polys import Poly, rootof
from sympy.polys.polyroots import roots
from sympy.polys.polytools import (cancel, degree)
from sympy.series import limit
from sympy.utilities.misc import filldedent
from mpmath.libmp.libmpf import prec_to_dps
__all__ = ['TransferFunction', 'Series', 'MIMOSeries', 'Parallel', 'MIMOParallel',
'Feedback', 'MIMOFeedback', 'TransferFunctionMatrix', 'StateSpace', 'gbt', 'bilinear', 'forward_diff', 'backward_diff',
'phase_margin', 'gain_margin']
def _roots(poly, var):
""" like roots, but works on higher-order polynomials. """
r = roots(poly, var, multiple=True)
n = degree(poly)
if len(r) != n:
r = [rootof(poly, var, k) for k in range(n)]
return r
def gbt(tf, sample_per, alpha):
r"""
Returns falling coefficients of H(z) from numerator and denominator.
Explanation
===========
Where H(z) is the corresponding discretized transfer function,
discretized with the generalised bilinear transformation method.
H(z) is obtained from the continuous transfer function H(s)
by substituting $s(z) = \frac{z-1}{T(\alpha z + (1-\alpha))}$ into H(s), where T is the
sample period.
Coefficients are falling, i.e. $H(z) = \frac{az+b}{cz+d}$ is returned
as [a, b], [c, d].
Examples
========
>>> from sympy.physics.control.lti import TransferFunction, gbt
>>> from sympy.abc import s, L, R, T
>>> tf = TransferFunction(1, s*L + R, s)
>>> numZ, denZ = gbt(tf, T, 0.5)
>>> numZ
[T/(2*(L + R*T/2)), T/(2*(L + R*T/2))]
>>> denZ
[1, (-L + R*T/2)/(L + R*T/2)]
>>> numZ, denZ = gbt(tf, T, 0)
>>> numZ
[T/L]
>>> denZ
[1, (-L + R*T)/L]
>>> numZ, denZ = gbt(tf, T, 1)
>>> numZ
[T/(L + R*T), 0]
>>> denZ
[1, -L/(L + R*T)]
>>> numZ, denZ = gbt(tf, T, 0.3)
>>> numZ
[3*T/(10*(L + 3*R*T/10)), 7*T/(10*(L + 3*R*T/10))]
>>> denZ
[1, (-L + 7*R*T/10)/(L + 3*R*T/10)]
References
==========
.. [1] https://www.polyu.edu.hk/ama/profile/gfzhang/Research/ZCC09_IJC.pdf
"""
if not tf.is_SISO:
raise NotImplementedError("Not implemented for MIMO systems.")
T = sample_per # and sample period T
s = tf.var
z = s # dummy discrete variable z
np = tf.num.as_poly(s).all_coeffs()
dp = tf.den.as_poly(s).all_coeffs()
alpha = Rational(alpha).limit_denominator(1000)
# The next line results from multiplying H(z) with z^N/z^N
N = max(len(np), len(dp)) - 1
num = Add(*[ T**(N-i) * c * (z-1)**i * (alpha * z + 1 - alpha)**(N-i) for c, i in zip(np[::-1], range(len(np))) ])
den = Add(*[ T**(N-i) * c * (z-1)**i * (alpha * z + 1 - alpha)**(N-i) for c, i in zip(dp[::-1], range(len(dp))) ])
num_coefs = num.as_poly(z).all_coeffs()
den_coefs = den.as_poly(z).all_coeffs()
para = den_coefs[0]
num_coefs = [coef/para for coef in num_coefs]
den_coefs = [coef/para for coef in den_coefs]
return num_coefs, den_coefs
def bilinear(tf, sample_per):
r"""
Returns falling coefficients of H(z) from numerator and denominator.
Explanation
===========
Where H(z) is the corresponding discretized transfer function,
discretized with the bilinear transform method.
H(z) is obtained from the continuous transfer function H(s)
by substituting $s(z) = \frac{2}{T}\frac{z-1}{z+1}$ into H(s), where T is the
sample period.
Coefficients are falling, i.e. $H(z) = \frac{az+b}{cz+d}$ is returned
as [a, b], [c, d].
Examples
========
>>> from sympy.physics.control.lti import TransferFunction, bilinear
>>> from sympy.abc import s, L, R, T
>>> tf = TransferFunction(1, s*L + R, s)
>>> numZ, denZ = bilinear(tf, T)
>>> numZ
[T/(2*(L + R*T/2)), T/(2*(L + R*T/2))]
>>> denZ
[1, (-L + R*T/2)/(L + R*T/2)]
"""
return gbt(tf, sample_per, S.Half)
def forward_diff(tf, sample_per):
r"""
Returns falling coefficients of H(z) from numerator and denominator.
Explanation
===========
Where H(z) is the corresponding discretized transfer function,
discretized with the forward difference transform method.
H(z) is obtained from the continuous transfer function H(s)
by substituting $s(z) = \frac{z-1}{T}$ into H(s), where T is the
sample period.
Coefficients are falling, i.e. $H(z) = \frac{az+b}{cz+d}$ is returned
as [a, b], [c, d].
Examples
========
>>> from sympy.physics.control.lti import TransferFunction, forward_diff
>>> from sympy.abc import s, L, R, T
>>> tf = TransferFunction(1, s*L + R, s)
>>> numZ, denZ = forward_diff(tf, T)
>>> numZ
[T/L]
>>> denZ
[1, (-L + R*T)/L]
"""
return gbt(tf, sample_per, S.Zero)
def backward_diff(tf, sample_per):
r"""
Returns falling coefficients of H(z) from numerator and denominator.
Explanation
===========
Where H(z) is the corresponding discretized transfer function,
discretized with the backward difference transform method.
H(z) is obtained from the continuous transfer function H(s)
by substituting $s(z) = \frac{z-1}{Tz}$ into H(s), where T is the
sample period.
Coefficients are falling, i.e. $H(z) = \frac{az+b}{cz+d}$ is returned
as [a, b], [c, d].
Examples
========
>>> from sympy.physics.control.lti import TransferFunction, backward_diff
>>> from sympy.abc import s, L, R, T
>>> tf = TransferFunction(1, s*L + R, s)
>>> numZ, denZ = backward_diff(tf, T)
>>> numZ
[T/(L + R*T), 0]
>>> denZ
[1, -L/(L + R*T)]
"""
return gbt(tf, sample_per, S.One)
def phase_margin(system):
r"""
Returns the phase margin of a continuous time system.
Only applicable to Transfer Functions which can generate valid bode plots.
Raises
======
NotImplementedError
When time delay terms are present in the system.
ValueError
When a SISO LTI system is not passed.
When more than one free symbol is present in the system.
The only variable in the transfer function should be
the variable of the Laplace transform.
Examples
========
>>> from sympy.physics.control import TransferFunction, phase_margin
>>> from sympy.abc import s
>>> tf = TransferFunction(1, s**3 + 2*s**2 + s, s)
>>> phase_margin(tf)
180*(-pi + atan((-1 + (-2*18**(1/3)/(9 + sqrt(93))**(1/3) + 12**(1/3)*(9 + sqrt(93))**(1/3))**2/36)/(-12**(1/3)*(9 + sqrt(93))**(1/3)/3 + 2*18**(1/3)/(3*(9 + sqrt(93))**(1/3)))))/pi + 180
>>> phase_margin(tf).n()
21.3863897518751
>>> tf1 = TransferFunction(s**3, s**2 + 5*s, s)
>>> phase_margin(tf1)
-180 + 180*(atan(sqrt(2)*(-51/10 - sqrt(101)/10)*sqrt(1 + sqrt(101))/(2*(sqrt(101)/2 + 51/2))) + pi)/pi
>>> phase_margin(tf1).n()
-25.1783920627277
>>> tf2 = TransferFunction(1, s + 1, s)
>>> phase_margin(tf2)
-180
See Also
========
gain_margin
References
==========
.. [1] https://en.wikipedia.org/wiki/Phase_margin
"""
from sympy.functions import arg
if not isinstance(system, SISOLinearTimeInvariant):
raise ValueError("Margins are only applicable for SISO LTI systems.")
_w = Dummy("w", real=True)
repl = I*_w
expr = system.to_expr()
len_free_symbols = len(expr.free_symbols)
if expr.has(exp):
raise NotImplementedError("Margins for systems with Time delay terms are not supported.")
elif len_free_symbols > 1:
raise ValueError("Extra degree of freedom found. Make sure"
" that there are no free symbols in the dynamical system other"
" than the variable of Laplace transform.")
w_expr = expr.subs({system.var: repl})
mag = 20*log(Abs(w_expr), 10)
mag_sol = list(solveset(mag, _w, Interval(0, oo, left_open=True)))
if (len(mag_sol) == 0):
pm = S(-180)
else:
wcp = mag_sol[0]
pm = ((arg(w_expr)*S(180)/pi).subs({_w:wcp}) + S(180)) % 360
if(pm >= 180):
pm = pm - 360
return pm
def gain_margin(system):
r"""
Returns the gain margin of a continuous time system.
Only applicable to Transfer Functions which can generate valid bode plots.
Raises
======
NotImplementedError
When time delay terms are present in the system.
ValueError
When a SISO LTI system is not passed.
When more than one free symbol is present in the system.
The only variable in the transfer function should be
the variable of the Laplace transform.
Examples
========
>>> from sympy.physics.control import TransferFunction, gain_margin
>>> from sympy.abc import s
>>> tf = TransferFunction(1, s**3 + 2*s**2 + s, s)
>>> gain_margin(tf)
20*log(2)/log(10)
>>> gain_margin(tf).n()
6.02059991327962
>>> tf1 = TransferFunction(s**3, s**2 + 5*s, s)
>>> gain_margin(tf1)
oo
See Also
========
phase_margin
References
==========
https://en.wikipedia.org/wiki/Bode_plot
"""
if not isinstance(system, SISOLinearTimeInvariant):
raise ValueError("Margins are only applicable for SISO LTI systems.")
_w = Dummy("w", real=True)
repl = I*_w
expr = system.to_expr()
len_free_symbols = len(expr.free_symbols)
if expr.has(exp):
raise NotImplementedError("Margins for systems with Time delay terms are not supported.")
elif len_free_symbols > 1:
raise ValueError("Extra degree of freedom found. Make sure"
" that there are no free symbols in the dynamical system other"
" than the variable of Laplace transform.")
w_expr = expr.subs({system.var: repl})
mag = 20*log(Abs(w_expr), 10)
phase = w_expr
phase_sol = list(solveset(numer(phase.as_real_imag()[1].cancel()),_w, Interval(0, oo, left_open = True)))
if (len(phase_sol) == 0):
gm = oo
else:
wcg = phase_sol[0]
gm = -mag.subs({_w:wcg})
return gm
class LinearTimeInvariant(Basic, EvalfMixin):
"""A common class for all the Linear Time-Invariant Dynamical Systems."""
_clstype: Type
# Users should not directly interact with this class.
def __new__(cls, *system, **kwargs):
if cls is LinearTimeInvariant:
raise NotImplementedError('The LTICommon class is not meant to be used directly.')
return super(LinearTimeInvariant, cls).__new__(cls, *system, **kwargs)
@classmethod
def _check_args(cls, args):
if not args:
raise ValueError("At least 1 argument must be passed.")
if not all(isinstance(arg, cls._clstype) for arg in args):
raise TypeError(f"All arguments must be of type {cls._clstype}.")
var_set = {arg.var for arg in args}
if len(var_set) != 1:
raise ValueError(filldedent(f"""
All transfer functions should use the same complex variable
of the Laplace transform. {len(var_set)} different
values found."""))
@property
def is_SISO(self):
"""Returns `True` if the passed LTI system is SISO else returns False."""
return self._is_SISO
class SISOLinearTimeInvariant(LinearTimeInvariant):
"""A common class for all the SISO Linear Time-Invariant Dynamical Systems."""
# Users should not directly interact with this class.
_is_SISO = True
class MIMOLinearTimeInvariant(LinearTimeInvariant):
"""A common class for all the MIMO Linear Time-Invariant Dynamical Systems."""
# Users should not directly interact with this class.
_is_SISO = False
SISOLinearTimeInvariant._clstype = SISOLinearTimeInvariant
MIMOLinearTimeInvariant._clstype = MIMOLinearTimeInvariant
def _check_other_SISO(func):
def wrapper(*args, **kwargs):
if not isinstance(args[-1], SISOLinearTimeInvariant):
return NotImplemented
else:
return func(*args, **kwargs)
return wrapper
def _check_other_MIMO(func):
def wrapper(*args, **kwargs):
if not isinstance(args[-1], MIMOLinearTimeInvariant):
return NotImplemented
else:
return func(*args, **kwargs)
return wrapper
class TransferFunction(SISOLinearTimeInvariant):
r"""
A class for representing LTI (Linear, time-invariant) systems that can be strictly described
by ratio of polynomials in the Laplace transform complex variable. The arguments
are ``num``, ``den``, and ``var``, where ``num`` and ``den`` are numerator and
denominator polynomials of the ``TransferFunction`` respectively, and the third argument is
a complex variable of the Laplace transform used by these polynomials of the transfer function.
``num`` and ``den`` can be either polynomials or numbers, whereas ``var``
has to be a :py:class:`~.Symbol`.
Explanation
===========
Generally, a dynamical system representing a physical model can be described in terms of Linear
Ordinary Differential Equations like -
$\small{b_{m}y^{\left(m\right)}+b_{m-1}y^{\left(m-1\right)}+\dots+b_{1}y^{\left(1\right)}+b_{0}y=
a_{n}x^{\left(n\right)}+a_{n-1}x^{\left(n-1\right)}+\dots+a_{1}x^{\left(1\right)}+a_{0}x}$
Here, $x$ is the input signal and $y$ is the output signal and superscript on both is the order of derivative
(not exponent). Derivative is taken with respect to the independent variable, $t$. Also, generally $m$ is greater
than $n$.
It is not feasible to analyse the properties of such systems in their native form therefore, we use
mathematical tools like Laplace transform to get a better perspective. Taking the Laplace transform
of both the sides in the equation (at zero initial conditions), we get -
$\small{\mathcal{L}[b_{m}y^{\left(m\right)}+b_{m-1}y^{\left(m-1\right)}+\dots+b_{1}y^{\left(1\right)}+b_{0}y]=
\mathcal{L}[a_{n}x^{\left(n\right)}+a_{n-1}x^{\left(n-1\right)}+\dots+a_{1}x^{\left(1\right)}+a_{0}x]}$
Using the linearity property of Laplace transform and also considering zero initial conditions
(i.e. $\small{y(0^{-}) = 0}$, $\small{y'(0^{-}) = 0}$ and so on), the equation
above gets translated to -
$\small{b_{m}\mathcal{L}[y^{\left(m\right)}]+\dots+b_{1}\mathcal{L}[y^{\left(1\right)}]+b_{0}\mathcal{L}[y]=
a_{n}\mathcal{L}[x^{\left(n\right)}]+\dots+a_{1}\mathcal{L}[x^{\left(1\right)}]+a_{0}\mathcal{L}[x]}$
Now, applying Derivative property of Laplace transform,
$\small{b_{m}s^{m}\mathcal{L}[y]+\dots+b_{1}s\mathcal{L}[y]+b_{0}\mathcal{L}[y]=
a_{n}s^{n}\mathcal{L}[x]+\dots+a_{1}s\mathcal{L}[x]+a_{0}\mathcal{L}[x]}$
Here, the superscript on $s$ is **exponent**. Note that the zero initial conditions assumption, mentioned above, is very important
and cannot be ignored otherwise the dynamical system cannot be considered time-independent and the simplified equation above
cannot be reached.
Collecting $\mathcal{L}[y]$ and $\mathcal{L}[x]$ terms from both the sides and taking the ratio
$\frac{ \mathcal{L}\left\{y\right\} }{ \mathcal{L}\left\{x\right\} }$, we get the typical rational form of transfer
function.
The numerator of the transfer function is, therefore, the Laplace transform of the output signal
(The signals are represented as functions of time) and similarly, the denominator
of the transfer function is the Laplace transform of the input signal. It is also a convention
to denote the input and output signal's Laplace transform with capital alphabets like shown below.
$H(s) = \frac{Y(s)}{X(s)} = \frac{ \mathcal{L}\left\{y(t)\right\} }{ \mathcal{L}\left\{x(t)\right\} }$
$s$, also known as complex frequency, is a complex variable in the Laplace domain. It corresponds to the
equivalent variable $t$, in the time domain. Transfer functions are sometimes also referred to as the Laplace
transform of the system's impulse response. Transfer function, $H$, is represented as a rational
function in $s$ like,
$H(s) =\ \frac{a_{n}s^{n}+a_{n-1}s^{n-1}+\dots+a_{1}s+a_{0}}{b_{m}s^{m}+b_{m-1}s^{m-1}+\dots+b_{1}s+b_{0}}$
Parameters
==========
num : Expr, Number
The numerator polynomial of the transfer function.
den : Expr, Number
The denominator polynomial of the transfer function.
var : Symbol
Complex variable of the Laplace transform used by the
polynomials of the transfer function.
Raises
======
TypeError
When ``var`` is not a Symbol or when ``num`` or ``den`` is not a
number or a polynomial.
ValueError
When ``den`` is zero.
Examples
========
>>> from sympy.abc import s, p, a
>>> from sympy.physics.control.lti import TransferFunction
>>> tf1 = TransferFunction(s + a, s**2 + s + 1, s)
>>> tf1
TransferFunction(a + s, s**2 + s + 1, s)
>>> tf1.num
a + s
>>> tf1.den
s**2 + s + 1
>>> tf1.var
s
>>> tf1.args
(a + s, s**2 + s + 1, s)
Any complex variable can be used for ``var``.
>>> tf2 = TransferFunction(a*p**3 - a*p**2 + s*p, p + a**2, p)
>>> tf2
TransferFunction(a*p**3 - a*p**2 + p*s, a**2 + p, p)
>>> tf3 = TransferFunction((p + 3)*(p - 1), (p - 1)*(p + 5), p)
>>> tf3
TransferFunction((p - 1)*(p + 3), (p - 1)*(p + 5), p)
To negate a transfer function the ``-`` operator can be prepended:
>>> tf4 = TransferFunction(-a + s, p**2 + s, p)
>>> -tf4
TransferFunction(a - s, p**2 + s, p)
>>> tf5 = TransferFunction(s**4 - 2*s**3 + 5*s + 4, s + 4, s)
>>> -tf5
TransferFunction(-s**4 + 2*s**3 - 5*s - 4, s + 4, s)
You can use a float or an integer (or other constants) as numerator and denominator:
>>> tf6 = TransferFunction(1/2, 4, s)
>>> tf6.num
0.500000000000000
>>> tf6.den
4
>>> tf6.var
s
>>> tf6.args
(0.5, 4, s)
You can take the integer power of a transfer function using the ``**`` operator:
>>> tf7 = TransferFunction(s + a, s - a, s)
>>> tf7**3
TransferFunction((a + s)**3, (-a + s)**3, s)
>>> tf7**0
TransferFunction(1, 1, s)
>>> tf8 = TransferFunction(p + 4, p - 3, p)
>>> tf8**-1
TransferFunction(p - 3, p + 4, p)
Addition, subtraction, and multiplication of transfer functions can form
unevaluated ``Series`` or ``Parallel`` objects.
>>> tf9 = TransferFunction(s + 1, s**2 + s + 1, s)
>>> tf10 = TransferFunction(s - p, s + 3, s)
>>> tf11 = TransferFunction(4*s**2 + 2*s - 4, s - 1, s)
>>> tf12 = TransferFunction(1 - s, s**2 + 4, s)
>>> tf9 + tf10
Parallel(TransferFunction(s + 1, s**2 + s + 1, s), TransferFunction(-p + s, s + 3, s))
>>> tf10 - tf11
Parallel(TransferFunction(-p + s, s + 3, s), TransferFunction(-4*s**2 - 2*s + 4, s - 1, s))
>>> tf9 * tf10
Series(TransferFunction(s + 1, s**2 + s + 1, s), TransferFunction(-p + s, s + 3, s))
>>> tf10 - (tf9 + tf12)
Parallel(TransferFunction(-p + s, s + 3, s), TransferFunction(-s - 1, s**2 + s + 1, s), TransferFunction(s - 1, s**2 + 4, s))
>>> tf10 - (tf9 * tf12)
Parallel(TransferFunction(-p + s, s + 3, s), Series(TransferFunction(-1, 1, s), TransferFunction(s + 1, s**2 + s + 1, s), TransferFunction(1 - s, s**2 + 4, s)))
>>> tf11 * tf10 * tf9
Series(TransferFunction(4*s**2 + 2*s - 4, s - 1, s), TransferFunction(-p + s, s + 3, s), TransferFunction(s + 1, s**2 + s + 1, s))
>>> tf9 * tf11 + tf10 * tf12
Parallel(Series(TransferFunction(s + 1, s**2 + s + 1, s), TransferFunction(4*s**2 + 2*s - 4, s - 1, s)), Series(TransferFunction(-p + s, s + 3, s), TransferFunction(1 - s, s**2 + 4, s)))
>>> (tf9 + tf12) * (tf10 + tf11)
Series(Parallel(TransferFunction(s + 1, s**2 + s + 1, s), TransferFunction(1 - s, s**2 + 4, s)), Parallel(TransferFunction(-p + s, s + 3, s), TransferFunction(4*s**2 + 2*s - 4, s - 1, s)))
These unevaluated ``Series`` or ``Parallel`` objects can convert into the
resultant transfer function using ``.doit()`` method or by ``.rewrite(TransferFunction)``.
>>> ((tf9 + tf10) * tf12).doit()
TransferFunction((1 - s)*((-p + s)*(s**2 + s + 1) + (s + 1)*(s + 3)), (s + 3)*(s**2 + 4)*(s**2 + s + 1), s)
>>> (tf9 * tf10 - tf11 * tf12).rewrite(TransferFunction)
TransferFunction(-(1 - s)*(s + 3)*(s**2 + s + 1)*(4*s**2 + 2*s - 4) + (-p + s)*(s - 1)*(s + 1)*(s**2 + 4), (s - 1)*(s + 3)*(s**2 + 4)*(s**2 + s + 1), s)
See Also
========
Feedback, Series, Parallel
References
==========
.. [1] https://en.wikipedia.org/wiki/Transfer_function
.. [2] https://en.wikipedia.org/wiki/Laplace_transform
"""
def __new__(cls, num, den, var):
num, den = _sympify(num), _sympify(den)
if not isinstance(var, Symbol):
raise TypeError("Variable input must be a Symbol.")
if den == 0:
raise ValueError("TransferFunction cannot have a zero denominator.")
if (((isinstance(num, (Expr, TransferFunction, Series, Parallel)) and num.has(Symbol)) or num.is_number) and
((isinstance(den, (Expr, TransferFunction, Series, Parallel)) and den.has(Symbol)) or den.is_number)):
return super(TransferFunction, cls).__new__(cls, num, den, var)
else:
raise TypeError("Unsupported type for numerator or denominator of TransferFunction.")
@classmethod
def from_rational_expression(cls, expr, var=None):
r"""
Creates a new ``TransferFunction`` efficiently from a rational expression.
Parameters
==========
expr : Expr, Number
The rational expression representing the ``TransferFunction``.
var : Symbol, optional
Complex variable of the Laplace transform used by the
polynomials of the transfer function.
Raises
======
ValueError
When ``expr`` is of type ``Number`` and optional parameter ``var``
is not passed.
When ``expr`` has more than one variables and an optional parameter
``var`` is not passed.
ZeroDivisionError
When denominator of ``expr`` is zero or it has ``ComplexInfinity``
in its numerator.
Examples
========
>>> from sympy.abc import s, p, a
>>> from sympy.physics.control.lti import TransferFunction
>>> expr1 = (s + 5)/(3*s**2 + 2*s + 1)
>>> tf1 = TransferFunction.from_rational_expression(expr1)
>>> tf1
TransferFunction(s + 5, 3*s**2 + 2*s + 1, s)
>>> expr2 = (a*p**3 - a*p**2 + s*p)/(p + a**2) # Expr with more than one variables
>>> tf2 = TransferFunction.from_rational_expression(expr2, p)
>>> tf2
TransferFunction(a*p**3 - a*p**2 + p*s, a**2 + p, p)
In case of conflict between two or more variables in a expression, SymPy will
raise a ``ValueError``, if ``var`` is not passed by the user.
>>> tf = TransferFunction.from_rational_expression((a + a*s)/(s**2 + s + 1))
Traceback (most recent call last):
...
ValueError: Conflicting values found for positional argument `var` ({a, s}). Specify it manually.
This can be corrected by specifying the ``var`` parameter manually.
>>> tf = TransferFunction.from_rational_expression((a + a*s)/(s**2 + s + 1), s)
>>> tf
TransferFunction(a*s + a, s**2 + s + 1, s)
``var`` also need to be specified when ``expr`` is a ``Number``
>>> tf3 = TransferFunction.from_rational_expression(10, s)
>>> tf3
TransferFunction(10, 1, s)
"""
expr = _sympify(expr)
if var is None:
_free_symbols = expr.free_symbols
_len_free_symbols = len(_free_symbols)
if _len_free_symbols == 1:
var = list(_free_symbols)[0]
elif _len_free_symbols == 0:
raise ValueError(filldedent("""
Positional argument `var` not found in the
TransferFunction defined. Specify it manually."""))
else:
raise ValueError(filldedent("""
Conflicting values found for positional argument `var` ({}).
Specify it manually.""".format(_free_symbols)))
_num, _den = expr.as_numer_denom()
if _den == 0 or _num.has(S.ComplexInfinity):
raise ZeroDivisionError("TransferFunction cannot have a zero denominator.")
return cls(_num, _den, var)
@classmethod
def from_coeff_lists(cls, num_list, den_list, var):
r"""
Creates a new ``TransferFunction`` efficiently from a list of coefficients.
Parameters
==========
num_list : Sequence
Sequence comprising of numerator coefficients.
den_list : Sequence
Sequence comprising of denominator coefficients.
var : Symbol
Complex variable of the Laplace transform used by the
polynomials of the transfer function.
Raises
======
ZeroDivisionError
When the constructed denominator is zero.
Examples
========
>>> from sympy.abc import s, p
>>> from sympy.physics.control.lti import TransferFunction
>>> num = [1, 0, 2]
>>> den = [3, 2, 2, 1]
>>> tf = TransferFunction.from_coeff_lists(num, den, s)
>>> tf
TransferFunction(s**2 + 2, 3*s**3 + 2*s**2 + 2*s + 1, s)
# Create a Transfer Function with more than one variable
>>> tf1 = TransferFunction.from_coeff_lists([p, 1], [2*p, 0, 4], s)
>>> tf1
TransferFunction(p*s + 1, 2*p*s**2 + 4, s)
"""
num_list = num_list[::-1]
den_list = den_list[::-1]
num_var_powers = [var**i for i in range(len(num_list))]
den_var_powers = [var**i for i in range(len(den_list))]
_num = sum(coeff * var_power for coeff, var_power in zip(num_list, num_var_powers))
_den = sum(coeff * var_power for coeff, var_power in zip(den_list, den_var_powers))
if _den == 0:
raise ZeroDivisionError("TransferFunction cannot have a zero denominator.")
return cls(_num, _den, var)
@classmethod
def from_zpk(cls, zeros, poles, gain, var):
r"""
Creates a new ``TransferFunction`` from given zeros, poles and gain.
Parameters
==========
zeros : Sequence
Sequence comprising of zeros of transfer function.
poles : Sequence
Sequence comprising of poles of transfer function.
gain : Number, Symbol, Expression
A scalar value specifying gain of the model.
var : Symbol
Complex variable of the Laplace transform used by the
polynomials of the transfer function.
Examples
========
>>> from sympy.abc import s, p, k
>>> from sympy.physics.control.lti import TransferFunction
>>> zeros = [1, 2, 3]
>>> poles = [6, 5, 4]
>>> gain = 7
>>> tf = TransferFunction.from_zpk(zeros, poles, gain, s)
>>> tf
TransferFunction(7*(s - 3)*(s - 2)*(s - 1), (s - 6)*(s - 5)*(s - 4), s)
# Create a Transfer Function with variable poles and zeros
>>> tf1 = TransferFunction.from_zpk([p, k], [p + k, p - k], 2, s)
>>> tf1
TransferFunction(2*(-k + s)*(-p + s), (-k - p + s)*(k - p + s), s)
# Complex poles or zeros are acceptable
>>> tf2 = TransferFunction.from_zpk([0], [1-1j, 1+1j, 2], -2, s)
>>> tf2
TransferFunction(-2*s, (s - 2)*(s - 1.0 - 1.0*I)*(s - 1.0 + 1.0*I), s)
"""
num_poly = 1
den_poly = 1
for zero in zeros:
num_poly *= var - zero
for pole in poles:
den_poly *= var - pole
return cls(gain*num_poly, den_poly, var)
@property
def num(self):
"""
Returns the numerator polynomial of the transfer function.
Examples
========
>>> from sympy.abc import s, p
>>> from sympy.physics.control.lti import TransferFunction
>>> G1 = TransferFunction(s**2 + p*s + 3, s - 4, s)
>>> G1.num
p*s + s**2 + 3
>>> G2 = TransferFunction((p + 5)*(p - 3), (p - 3)*(p + 1), p)
>>> G2.num
(p - 3)*(p + 5)
"""
return self.args[0]
@property
def den(self):
"""
Returns the denominator polynomial of the transfer function.
Examples
========
>>> from sympy.abc import s, p
>>> from sympy.physics.control.lti import TransferFunction
>>> G1 = TransferFunction(s + 4, p**3 - 2*p + 4, s)
>>> G1.den
p**3 - 2*p + 4
>>> G2 = TransferFunction(3, 4, s)
>>> G2.den
4
"""
return self.args[1]
@property
def var(self):
"""
Returns the complex variable of the Laplace transform used by the polynomials of
the transfer function.
Examples
========
>>> from sympy.abc import s, p
>>> from sympy.physics.control.lti import TransferFunction
>>> G1 = TransferFunction(p**2 + 2*p + 4, p - 6, p)
>>> G1.var
p
>>> G2 = TransferFunction(0, s - 5, s)
>>> G2.var
s
"""
return self.args[2]
def _eval_subs(self, old, new):
arg_num = self.num.subs(old, new)
arg_den = self.den.subs(old, new)
argnew = TransferFunction(arg_num, arg_den, self.var)
return self if old == self.var else argnew
def _eval_evalf(self, prec):
return TransferFunction(
self.num._eval_evalf(prec),
self.den._eval_evalf(prec),
self.var)
def _eval_simplify(self, **kwargs):
tf = cancel(Mul(self.num, 1/self.den, evaluate=False), expand=False).as_numer_denom()
num_, den_ = tf[0], tf[1]
return TransferFunction(num_, den_, self.var)
def _eval_rewrite_as_StateSpace(self, *args):
"""
Returns the equivalent space space model of the transfer function model.
The state space model will be returned in the controllable cannonical form.
Unlike the space state to transfer function model conversion, the transfer function
to state space model conversion is not unique. There can be multiple state space
representations of a given transfer function model.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control import TransferFunction, StateSpace
>>> tf = TransferFunction(s**2 + 1, s**3 + 2*s + 10, s)
>>> tf.rewrite(StateSpace)
StateSpace(Matrix([
[ 0, 1, 0],
[ 0, 0, 1],
[-10, -2, 0]]), Matrix([
[0],
[0],
[1]]), Matrix([[1, 0, 1]]), Matrix([[0]]))
"""
if not self.is_proper:
raise ValueError("Transfer Function must be proper.")
num_poly = Poly(self.num, self.var)
den_poly = Poly(self.den, self.var)
n = den_poly.degree()
num_coeffs = num_poly.all_coeffs()
den_coeffs = den_poly.all_coeffs()
diff = n - num_poly.degree()
num_coeffs = [0]*diff + num_coeffs
a = den_coeffs[1:]
a_mat = Matrix([[(-1)*coefficient/den_coeffs[0] for coefficient in reversed(a)]])
vert = zeros(n-1, 1)
mat = eye(n-1)
A = vert.row_join(mat)
A = A.col_join(a_mat)
B = zeros(n, 1)
B[n-1] = 1
i = n
C = []
while(i > 0):
C.append(num_coeffs[i] - den_coeffs[i]*num_coeffs[0])
i -= 1
C = Matrix([C])
D = Matrix([num_coeffs[0]])
return StateSpace(A, B, C, D)
def expand(self):
"""
Returns the transfer function with numerator and denominator
in expanded form.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction
>>> G1 = TransferFunction((a - s)**2, (s**2 + a)**2, s)
>>> G1.expand()
TransferFunction(a**2 - 2*a*s + s**2, a**2 + 2*a*s**2 + s**4, s)
>>> G2 = TransferFunction((p + 3*b)*(p - b), (p - b)*(p + 2*b), p)
>>> G2.expand()
TransferFunction(-3*b**2 + 2*b*p + p**2, -2*b**2 + b*p + p**2, p)
"""
return TransferFunction(expand(self.num), expand(self.den), self.var)
def dc_gain(self):
"""
Computes the gain of the response as the frequency approaches zero.
The DC gain is infinite for systems with pure integrators.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction
>>> tf1 = TransferFunction(s + 3, s**2 - 9, s)
>>> tf1.dc_gain()
-1/3
>>> tf2 = TransferFunction(p**2, p - 3 + p**3, p)
>>> tf2.dc_gain()
0
>>> tf3 = TransferFunction(a*p**2 - b, s + b, s)
>>> tf3.dc_gain()
(a*p**2 - b)/b
>>> tf4 = TransferFunction(1, s, s)
>>> tf4.dc_gain()
oo
"""
m = Mul(self.num, Pow(self.den, -1, evaluate=False), evaluate=False)
return limit(m, self.var, 0)
def poles(self):
"""
Returns the poles of a transfer function.
Examples
========
>>> from sympy.abc import s, p, a
>>> from sympy.physics.control.lti import TransferFunction
>>> tf1 = TransferFunction((p + 3)*(p - 1), (p - 1)*(p + 5), p)
>>> tf1.poles()
[-5, 1]
>>> tf2 = TransferFunction((1 - s)**2, (s**2 + 1)**2, s)
>>> tf2.poles()
[I, I, -I, -I]
>>> tf3 = TransferFunction(s**2, a*s + p, s)
>>> tf3.poles()
[-p/a]
"""
return _roots(Poly(self.den, self.var), self.var)
def zeros(self):
"""
Returns the zeros of a transfer function.
Examples
========
>>> from sympy.abc import s, p, a
>>> from sympy.physics.control.lti import TransferFunction
>>> tf1 = TransferFunction((p + 3)*(p - 1), (p - 1)*(p + 5), p)
>>> tf1.zeros()
[-3, 1]
>>> tf2 = TransferFunction((1 - s)**2, (s**2 + 1)**2, s)
>>> tf2.zeros()
[1, 1]
>>> tf3 = TransferFunction(s**2, a*s + p, s)
>>> tf3.zeros()
[0, 0]
"""
return _roots(Poly(self.num, self.var), self.var)
def eval_frequency(self, other):
"""
Returns the system response at any point in the real or complex plane.
Examples
========
>>> from sympy.abc import s, p, a
>>> from sympy.physics.control.lti import TransferFunction
>>> from sympy import I
>>> tf1 = TransferFunction(1, s**2 + 2*s + 1, s)
>>> omega = 0.1
>>> tf1.eval_frequency(I*omega)
1/(0.99 + 0.2*I)
>>> tf2 = TransferFunction(s**2, a*s + p, s)
>>> tf2.eval_frequency(2)
4/(2*a + p)
>>> tf2.eval_frequency(I*2)
-4/(2*I*a + p)
"""
arg_num = self.num.subs(self.var, other)
arg_den = self.den.subs(self.var, other)
argnew = TransferFunction(arg_num, arg_den, self.var).to_expr()
return argnew.expand()
def is_stable(self):
"""
Returns True if the transfer function is asymptotically stable; else False.
This would not check the marginal or conditional stability of the system.
Examples
========
>>> from sympy.abc import s, p, a
>>> from sympy import symbols
>>> from sympy.physics.control.lti import TransferFunction
>>> q, r = symbols('q, r', negative=True)
>>> tf1 = TransferFunction((1 - s)**2, (s + 1)**2, s)
>>> tf1.is_stable()
True
>>> tf2 = TransferFunction((1 - p)**2, (s**2 + 1)**2, s)
>>> tf2.is_stable()
False
>>> tf3 = TransferFunction(4, q*s - r, s)
>>> tf3.is_stable()
False
>>> tf4 = TransferFunction(p + 1, a*p - s**2, p)
>>> tf4.is_stable() is None # Not enough info about the symbols to determine stability
True
"""
return fuzzy_and(pole.as_real_imag()[0].is_negative for pole in self.poles())
def __add__(self, other):
if isinstance(other, (TransferFunction, Series)):
if not self.var == other.var:
raise ValueError(filldedent("""
All the transfer functions should use the same complex variable
of the Laplace transform."""))
return Parallel(self, other)
elif isinstance(other, Parallel):
if not self.var == other.var:
raise ValueError(filldedent("""
All the transfer functions should use the same complex variable
of the Laplace transform."""))
arg_list = list(other.args)
return Parallel(self, *arg_list)
else:
raise ValueError("TransferFunction cannot be added with {}.".
format(type(other)))
def __radd__(self, other):
return self + other
def __sub__(self, other):
if isinstance(other, (TransferFunction, Series)):
if not self.var == other.var:
raise ValueError(filldedent("""
All the transfer functions should use the same complex variable
of the Laplace transform."""))
return Parallel(self, -other)
elif isinstance(other, Parallel):
if not self.var == other.var:
raise ValueError(filldedent("""
All the transfer functions should use the same complex variable
of the Laplace transform."""))
arg_list = [-i for i in list(other.args)]
return Parallel(self, *arg_list)
else:
raise ValueError("{} cannot be subtracted from a TransferFunction."
.format(type(other)))
def __rsub__(self, other):
return -self + other
def __mul__(self, other):
if isinstance(other, (TransferFunction, Parallel)):
if not self.var == other.var:
raise ValueError(filldedent("""
All the transfer functions should use the same complex variable
of the Laplace transform."""))
return Series(self, other)
elif isinstance(other, Series):
if not self.var == other.var:
raise ValueError(filldedent("""
All the transfer functions should use the same complex variable
of the Laplace transform."""))
arg_list = list(other.args)
return Series(self, *arg_list)
else:
raise ValueError("TransferFunction cannot be multiplied with {}."
.format(type(other)))
__rmul__ = __mul__
def __truediv__(self, other):
if isinstance(other, TransferFunction):
if not self.var == other.var:
raise ValueError(filldedent("""
All the transfer functions should use the same complex variable
of the Laplace transform."""))
return Series(self, TransferFunction(other.den, other.num, self.var))
elif (isinstance(other, Parallel) and len(other.args
) == 2 and isinstance(other.args[0], TransferFunction)
and isinstance(other.args[1], (Series, TransferFunction))):
if not self.var == other.var:
raise ValueError(filldedent("""
Both TransferFunction and Parallel should use the
same complex variable of the Laplace transform."""))
if other.args[1] == self:
# plant and controller with unit feedback.
return Feedback(self, other.args[0])
other_arg_list = list(other.args[1].args) if isinstance(
other.args[1], Series) else other.args[1]
if other_arg_list == other.args[1]:
return Feedback(self, other_arg_list)
elif self in other_arg_list:
other_arg_list.remove(self)
else:
return Feedback(self, Series(*other_arg_list))
if len(other_arg_list) == 1:
return Feedback(self, *other_arg_list)
else:
return Feedback(self, Series(*other_arg_list))
else:
raise ValueError("TransferFunction cannot be divided by {}.".
format(type(other)))
__rtruediv__ = __truediv__
def __pow__(self, p):
p = sympify(p)
if not p.is_Integer:
raise ValueError("Exponent must be an integer.")
if p is S.Zero:
return TransferFunction(1, 1, self.var)
elif p > 0:
num_, den_ = self.num**p, self.den**p
else:
p = abs(p)
num_, den_ = self.den**p, self.num**p
return TransferFunction(num_, den_, self.var)
def __neg__(self):
return TransferFunction(-self.num, self.den, self.var)
@property
def is_proper(self):
"""
Returns True if degree of the numerator polynomial is less than
or equal to degree of the denominator polynomial, else False.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction
>>> tf1 = TransferFunction(b*s**2 + p**2 - a*p + s, b - p**2, s)
>>> tf1.is_proper
False
>>> tf2 = TransferFunction(p**2 - 4*p, p**3 + 3*p + 2, p)
>>> tf2.is_proper
True
"""
return degree(self.num, self.var) <= degree(self.den, self.var)
@property
def is_strictly_proper(self):
"""
Returns True if degree of the numerator polynomial is strictly less
than degree of the denominator polynomial, else False.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf1.is_strictly_proper
False
>>> tf2 = TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)
>>> tf2.is_strictly_proper
True
"""
return degree(self.num, self.var) < degree(self.den, self.var)
@property
def is_biproper(self):
"""
Returns True if degree of the numerator polynomial is equal to
degree of the denominator polynomial, else False.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf1.is_biproper
True
>>> tf2 = TransferFunction(p**2, p + a, p)
>>> tf2.is_biproper
False
"""
return degree(self.num, self.var) == degree(self.den, self.var)
def to_expr(self):
"""
Converts a ``TransferFunction`` object to SymPy Expr.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction
>>> from sympy import Expr
>>> tf1 = TransferFunction(s, a*s**2 + 1, s)
>>> tf1.to_expr()
s/(a*s**2 + 1)
>>> isinstance(_, Expr)
True
>>> tf2 = TransferFunction(1, (p + 3*b)*(b - p), p)
>>> tf2.to_expr()
1/((b - p)*(3*b + p))
>>> tf3 = TransferFunction((s - 2)*(s - 3), (s - 1)*(s - 2)*(s - 3), s)
>>> tf3.to_expr()
((s - 3)*(s - 2))/(((s - 3)*(s - 2)*(s - 1)))
"""
if self.num != 1:
return Mul(self.num, Pow(self.den, -1, evaluate=False), evaluate=False)
else:
return Pow(self.den, -1, evaluate=False)
def _flatten_args(args, _cls):
temp_args = []
for arg in args:
if isinstance(arg, _cls):
temp_args.extend(arg.args)
else:
temp_args.append(arg)
return tuple(temp_args)
def _dummify_args(_arg, var):
dummy_dict = {}
dummy_arg_list = []
for arg in _arg:
_s = Dummy()
dummy_dict[_s] = var
dummy_arg = arg.subs({var: _s})
dummy_arg_list.append(dummy_arg)
return dummy_arg_list, dummy_dict
class Series(SISOLinearTimeInvariant):
r"""
A class for representing a series configuration of SISO systems.
Parameters
==========
args : SISOLinearTimeInvariant
SISO systems in a series configuration.
evaluate : Boolean, Keyword
When passed ``True``, returns the equivalent
``Series(*args).doit()``. Set to ``False`` by default.
Raises
======
ValueError
When no argument is passed.
``var`` attribute is not same for every system.
TypeError
Any of the passed ``*args`` has unsupported type
A combination of SISO and MIMO systems is
passed. There should be homogeneity in the
type of systems passed, SISO in this case.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Series, Parallel
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)
>>> tf3 = TransferFunction(p**2, p + s, s)
>>> S1 = Series(tf1, tf2)
>>> S1
Series(TransferFunction(a*p**2 + b*s, -p + s, s), TransferFunction(s**3 - 2, s**4 + 5*s + 6, s))
>>> S1.var
s
>>> S2 = Series(tf2, Parallel(tf3, -tf1))
>>> S2
Series(TransferFunction(s**3 - 2, s**4 + 5*s + 6, s), Parallel(TransferFunction(p**2, p + s, s), TransferFunction(-a*p**2 - b*s, -p + s, s)))
>>> S2.var
s
>>> S3 = Series(Parallel(tf1, tf2), Parallel(tf2, tf3))
>>> S3
Series(Parallel(TransferFunction(a*p**2 + b*s, -p + s, s), TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)), Parallel(TransferFunction(s**3 - 2, s**4 + 5*s + 6, s), TransferFunction(p**2, p + s, s)))
>>> S3.var
s
You can get the resultant transfer function by using ``.doit()`` method:
>>> S3 = Series(tf1, tf2, -tf3)
>>> S3.doit()
TransferFunction(-p**2*(s**3 - 2)*(a*p**2 + b*s), (-p + s)*(p + s)*(s**4 + 5*s + 6), s)
>>> S4 = Series(tf2, Parallel(tf1, -tf3))
>>> S4.doit()
TransferFunction((s**3 - 2)*(-p**2*(-p + s) + (p + s)*(a*p**2 + b*s)), (-p + s)*(p + s)*(s**4 + 5*s + 6), s)
Notes
=====
All the transfer functions should use the same complex variable
``var`` of the Laplace transform.
See Also
========
MIMOSeries, Parallel, TransferFunction, Feedback
"""
def __new__(cls, *args, evaluate=False):
args = _flatten_args(args, Series)
cls._check_args(args)
obj = super().__new__(cls, *args)
return obj.doit() if evaluate else obj
@property
def var(self):
"""
Returns the complex variable used by all the transfer functions.
Examples
========
>>> from sympy.abc import p
>>> from sympy.physics.control.lti import TransferFunction, Series, Parallel
>>> G1 = TransferFunction(p**2 + 2*p + 4, p - 6, p)
>>> G2 = TransferFunction(p, 4 - p, p)
>>> G3 = TransferFunction(0, p**4 - 1, p)
>>> Series(G1, G2).var
p
>>> Series(-G3, Parallel(G1, G2)).var
p
"""
return self.args[0].var
def doit(self, **hints):
"""
Returns the resultant transfer function obtained after evaluating
the transfer functions in series configuration.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Series
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)
>>> Series(tf2, tf1).doit()
TransferFunction((s**3 - 2)*(a*p**2 + b*s), (-p + s)*(s**4 + 5*s + 6), s)
>>> Series(-tf1, -tf2).doit()
TransferFunction((2 - s**3)*(-a*p**2 - b*s), (-p + s)*(s**4 + 5*s + 6), s)
"""
_num_arg = (arg.doit().num for arg in self.args)
_den_arg = (arg.doit().den for arg in self.args)
res_num = Mul(*_num_arg, evaluate=True)
res_den = Mul(*_den_arg, evaluate=True)
return TransferFunction(res_num, res_den, self.var)
def _eval_rewrite_as_TransferFunction(self, *args, **kwargs):
return self.doit()
@_check_other_SISO
def __add__(self, other):
if isinstance(other, Parallel):
arg_list = list(other.args)
return Parallel(self, *arg_list)
return Parallel(self, other)
__radd__ = __add__
@_check_other_SISO
def __sub__(self, other):
return self + (-other)
def __rsub__(self, other):
return -self + other
@_check_other_SISO
def __mul__(self, other):
arg_list = list(self.args)
return Series(*arg_list, other)
def __truediv__(self, other):
if isinstance(other, TransferFunction):
return Series(*self.args, TransferFunction(other.den, other.num, other.var))
elif isinstance(other, Series):
tf_self = self.rewrite(TransferFunction)
tf_other = other.rewrite(TransferFunction)
return tf_self / tf_other
elif (isinstance(other, Parallel) and len(other.args) == 2
and isinstance(other.args[0], TransferFunction) and isinstance(other.args[1], Series)):
if not self.var == other.var:
raise ValueError(filldedent("""
All the transfer functions should use the same complex variable
of the Laplace transform."""))
self_arg_list = set(self.args)
other_arg_list = set(other.args[1].args)
res = list(self_arg_list ^ other_arg_list)
if len(res) == 0:
return Feedback(self, other.args[0])
elif len(res) == 1:
return Feedback(self, *res)
else:
return Feedback(self, Series(*res))
else:
raise ValueError("This transfer function expression is invalid.")
def __neg__(self):
return Series(TransferFunction(-1, 1, self.var), self)
def to_expr(self):
"""Returns the equivalent ``Expr`` object."""
return Mul(*(arg.to_expr() for arg in self.args), evaluate=False)
@property
def is_proper(self):
"""
Returns True if degree of the numerator polynomial of the resultant transfer
function is less than or equal to degree of the denominator polynomial of
the same, else False.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Series
>>> tf1 = TransferFunction(b*s**2 + p**2 - a*p + s, b - p**2, s)
>>> tf2 = TransferFunction(p**2 - 4*p, p**3 + 3*s + 2, s)
>>> tf3 = TransferFunction(s, s**2 + s + 1, s)
>>> S1 = Series(-tf2, tf1)
>>> S1.is_proper
False
>>> S2 = Series(tf1, tf2, tf3)
>>> S2.is_proper
True
"""
return self.doit().is_proper
@property
def is_strictly_proper(self):
"""
Returns True if degree of the numerator polynomial of the resultant transfer
function is strictly less than degree of the denominator polynomial of
the same, else False.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Series
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(s**3 - 2, s**2 + 5*s + 6, s)
>>> tf3 = TransferFunction(1, s**2 + s + 1, s)
>>> S1 = Series(tf1, tf2)
>>> S1.is_strictly_proper
False
>>> S2 = Series(tf1, tf2, tf3)
>>> S2.is_strictly_proper
True
"""
return self.doit().is_strictly_proper
@property
def is_biproper(self):
r"""
Returns True if degree of the numerator polynomial of the resultant transfer
function is equal to degree of the denominator polynomial of
the same, else False.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Series
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(p, s**2, s)
>>> tf3 = TransferFunction(s**2, 1, s)
>>> S1 = Series(tf1, -tf2)
>>> S1.is_biproper
False
>>> S2 = Series(tf2, tf3)
>>> S2.is_biproper
True
"""
return self.doit().is_biproper
def _mat_mul_compatible(*args):
"""To check whether shapes are compatible for matrix mul."""
return all(args[i].num_outputs == args[i+1].num_inputs for i in range(len(args)-1))
class MIMOSeries(MIMOLinearTimeInvariant):
r"""
A class for representing a series configuration of MIMO systems.
Parameters
==========
args : MIMOLinearTimeInvariant
MIMO systems in a series configuration.
evaluate : Boolean, Keyword
When passed ``True``, returns the equivalent
``MIMOSeries(*args).doit()``. Set to ``False`` by default.
Raises
======
ValueError
When no argument is passed.
``var`` attribute is not same for every system.
``num_outputs`` of the MIMO system is not equal to the
``num_inputs`` of its adjacent MIMO system. (Matrix
multiplication constraint, basically)
TypeError
Any of the passed ``*args`` has unsupported type
A combination of SISO and MIMO systems is
passed. There should be homogeneity in the
type of systems passed, MIMO in this case.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import MIMOSeries, TransferFunctionMatrix
>>> from sympy import Matrix, pprint
>>> mat_a = Matrix([[5*s], [5]]) # 2 Outputs 1 Input
>>> mat_b = Matrix([[5, 1/(6*s**2)]]) # 1 Output 2 Inputs
>>> mat_c = Matrix([[1, s], [5/s, 1]]) # 2 Outputs 2 Inputs
>>> tfm_a = TransferFunctionMatrix.from_Matrix(mat_a, s)
>>> tfm_b = TransferFunctionMatrix.from_Matrix(mat_b, s)
>>> tfm_c = TransferFunctionMatrix.from_Matrix(mat_c, s)
>>> MIMOSeries(tfm_c, tfm_b, tfm_a)
MIMOSeries(TransferFunctionMatrix(((TransferFunction(1, 1, s), TransferFunction(s, 1, s)), (TransferFunction(5, s, s), TransferFunction(1, 1, s)))), TransferFunctionMatrix(((TransferFunction(5, 1, s), TransferFunction(1, 6*s**2, s)),)), TransferFunctionMatrix(((TransferFunction(5*s, 1, s),), (TransferFunction(5, 1, s),))))
>>> pprint(_, use_unicode=False) # For Better Visualization
[5*s] [1 s]
[---] [5 1 ] [- -]
[ 1 ] [- ----] [1 1]
[ ] *[1 2] *[ ]
[ 5 ] [ 6*s ]{t} [5 1]
[ - ] [- -]
[ 1 ]{t} [s 1]{t}
>>> MIMOSeries(tfm_c, tfm_b, tfm_a).doit()
TransferFunctionMatrix(((TransferFunction(150*s**4 + 25*s, 6*s**3, s), TransferFunction(150*s**4 + 5*s, 6*s**2, s)), (TransferFunction(150*s**3 + 25, 6*s**3, s), TransferFunction(150*s**3 + 5, 6*s**2, s))))
>>> pprint(_, use_unicode=False) # (2 Inputs -A-> 2 Outputs) -> (2 Inputs -B-> 1 Output) -> (1 Input -C-> 2 Outputs) is equivalent to (2 Inputs -Series Equivalent-> 2 Outputs).
[ 4 4 ]
[150*s + 25*s 150*s + 5*s]
[------------- ------------]
[ 3 2 ]
[ 6*s 6*s ]
[ ]
[ 3 3 ]
[ 150*s + 25 150*s + 5 ]
[ ----------- ---------- ]
[ 3 2 ]
[ 6*s 6*s ]{t}
Notes
=====
All the transfer function matrices should use the same complex variable ``var`` of the Laplace transform.
``MIMOSeries(A, B)`` is not equivalent to ``A*B``. It is always in the reverse order, that is ``B*A``.
See Also
========
Series, MIMOParallel
"""
def __new__(cls, *args, evaluate=False):
cls._check_args(args)
if _mat_mul_compatible(*args):
obj = super().__new__(cls, *args)
else:
raise ValueError(filldedent("""
Number of input signals do not match the number
of output signals of adjacent systems for some args."""))
return obj.doit() if evaluate else obj
@property
def var(self):
"""
Returns the complex variable used by all the transfer functions.
Examples
========
>>> from sympy.abc import p
>>> from sympy.physics.control.lti import TransferFunction, MIMOSeries, TransferFunctionMatrix
>>> G1 = TransferFunction(p**2 + 2*p + 4, p - 6, p)
>>> G2 = TransferFunction(p, 4 - p, p)
>>> G3 = TransferFunction(0, p**4 - 1, p)
>>> tfm_1 = TransferFunctionMatrix([[G1, G2, G3]])
>>> tfm_2 = TransferFunctionMatrix([[G1], [G2], [G3]])
>>> MIMOSeries(tfm_2, tfm_1).var
p
"""
return self.args[0].var
@property
def num_inputs(self):
"""Returns the number of input signals of the series system."""
return self.args[0].num_inputs
@property
def num_outputs(self):
"""Returns the number of output signals of the series system."""
return self.args[-1].num_outputs
@property
def shape(self):
"""Returns the shape of the equivalent MIMO system."""
return self.num_outputs, self.num_inputs
def doit(self, cancel=False, **kwargs):
"""
Returns the resultant transfer function matrix obtained after evaluating
the MIMO systems arranged in a series configuration.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, MIMOSeries, TransferFunctionMatrix
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)
>>> tfm1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf2]])
>>> tfm2 = TransferFunctionMatrix([[tf2, tf1], [tf1, tf1]])
>>> MIMOSeries(tfm2, tfm1).doit()
TransferFunctionMatrix(((TransferFunction(2*(-p + s)*(s**3 - 2)*(a*p**2 + b*s)*(s**4 + 5*s + 6), (-p + s)**2*(s**4 + 5*s + 6)**2, s), TransferFunction((-p + s)**2*(s**3 - 2)*(a*p**2 + b*s) + (-p + s)*(a*p**2 + b*s)**2*(s**4 + 5*s + 6), (-p + s)**3*(s**4 + 5*s + 6), s)), (TransferFunction((-p + s)*(s**3 - 2)**2*(s**4 + 5*s + 6) + (s**3 - 2)*(a*p**2 + b*s)*(s**4 + 5*s + 6)**2, (-p + s)*(s**4 + 5*s + 6)**3, s), TransferFunction(2*(s**3 - 2)*(a*p**2 + b*s), (-p + s)*(s**4 + 5*s + 6), s))))
"""
_arg = (arg.doit()._expr_mat for arg in reversed(self.args))
if cancel:
res = MatMul(*_arg, evaluate=True)
return TransferFunctionMatrix.from_Matrix(res, self.var)
_dummy_args, _dummy_dict = _dummify_args(_arg, self.var)
res = MatMul(*_dummy_args, evaluate=True)
temp_tfm = TransferFunctionMatrix.from_Matrix(res, self.var)
return temp_tfm.subs(_dummy_dict)
def _eval_rewrite_as_TransferFunctionMatrix(self, *args, **kwargs):
return self.doit()
@_check_other_MIMO
def __add__(self, other):
if isinstance(other, MIMOParallel):
arg_list = list(other.args)
return MIMOParallel(self, *arg_list)
return MIMOParallel(self, other)
__radd__ = __add__
@_check_other_MIMO
def __sub__(self, other):
return self + (-other)
def __rsub__(self, other):
return -self + other
@_check_other_MIMO
def __mul__(self, other):
if isinstance(other, MIMOSeries):
self_arg_list = list(self.args)
other_arg_list = list(other.args)
return MIMOSeries(*other_arg_list, *self_arg_list) # A*B = MIMOSeries(B, A)
arg_list = list(self.args)
return MIMOSeries(other, *arg_list)
def __neg__(self):
arg_list = list(self.args)
arg_list[0] = -arg_list[0]
return MIMOSeries(*arg_list)
class Parallel(SISOLinearTimeInvariant):
r"""
A class for representing a parallel configuration of SISO systems.
Parameters
==========
args : SISOLinearTimeInvariant
SISO systems in a parallel arrangement.
evaluate : Boolean, Keyword
When passed ``True``, returns the equivalent
``Parallel(*args).doit()``. Set to ``False`` by default.
Raises
======
ValueError
When no argument is passed.
``var`` attribute is not same for every system.
TypeError
Any of the passed ``*args`` has unsupported type
A combination of SISO and MIMO systems is
passed. There should be homogeneity in the
type of systems passed.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Parallel, Series
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)
>>> tf3 = TransferFunction(p**2, p + s, s)
>>> P1 = Parallel(tf1, tf2)
>>> P1
Parallel(TransferFunction(a*p**2 + b*s, -p + s, s), TransferFunction(s**3 - 2, s**4 + 5*s + 6, s))
>>> P1.var
s
>>> P2 = Parallel(tf2, Series(tf3, -tf1))
>>> P2
Parallel(TransferFunction(s**3 - 2, s**4 + 5*s + 6, s), Series(TransferFunction(p**2, p + s, s), TransferFunction(-a*p**2 - b*s, -p + s, s)))
>>> P2.var
s
>>> P3 = Parallel(Series(tf1, tf2), Series(tf2, tf3))
>>> P3
Parallel(Series(TransferFunction(a*p**2 + b*s, -p + s, s), TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)), Series(TransferFunction(s**3 - 2, s**4 + 5*s + 6, s), TransferFunction(p**2, p + s, s)))
>>> P3.var
s
You can get the resultant transfer function by using ``.doit()`` method:
>>> Parallel(tf1, tf2, -tf3).doit()
TransferFunction(-p**2*(-p + s)*(s**4 + 5*s + 6) + (-p + s)*(p + s)*(s**3 - 2) + (p + s)*(a*p**2 + b*s)*(s**4 + 5*s + 6), (-p + s)*(p + s)*(s**4 + 5*s + 6), s)
>>> Parallel(tf2, Series(tf1, -tf3)).doit()
TransferFunction(-p**2*(a*p**2 + b*s)*(s**4 + 5*s + 6) + (-p + s)*(p + s)*(s**3 - 2), (-p + s)*(p + s)*(s**4 + 5*s + 6), s)
Notes
=====
All the transfer functions should use the same complex variable
``var`` of the Laplace transform.
See Also
========
Series, TransferFunction, Feedback
"""
def __new__(cls, *args, evaluate=False):
args = _flatten_args(args, Parallel)
cls._check_args(args)
obj = super().__new__(cls, *args)
return obj.doit() if evaluate else obj
@property
def var(self):
"""
Returns the complex variable used by all the transfer functions.
Examples
========
>>> from sympy.abc import p
>>> from sympy.physics.control.lti import TransferFunction, Parallel, Series
>>> G1 = TransferFunction(p**2 + 2*p + 4, p - 6, p)
>>> G2 = TransferFunction(p, 4 - p, p)
>>> G3 = TransferFunction(0, p**4 - 1, p)
>>> Parallel(G1, G2).var
p
>>> Parallel(-G3, Series(G1, G2)).var
p
"""
return self.args[0].var
def doit(self, **hints):
"""
Returns the resultant transfer function obtained after evaluating
the transfer functions in parallel configuration.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Parallel
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)
>>> Parallel(tf2, tf1).doit()
TransferFunction((-p + s)*(s**3 - 2) + (a*p**2 + b*s)*(s**4 + 5*s + 6), (-p + s)*(s**4 + 5*s + 6), s)
>>> Parallel(-tf1, -tf2).doit()
TransferFunction((2 - s**3)*(-p + s) + (-a*p**2 - b*s)*(s**4 + 5*s + 6), (-p + s)*(s**4 + 5*s + 6), s)
"""
_arg = (arg.doit().to_expr() for arg in self.args)
res = Add(*_arg).as_numer_denom()
return TransferFunction(*res, self.var)
def _eval_rewrite_as_TransferFunction(self, *args, **kwargs):
return self.doit()
@_check_other_SISO
def __add__(self, other):
self_arg_list = list(self.args)
return Parallel(*self_arg_list, other)
__radd__ = __add__
@_check_other_SISO
def __sub__(self, other):
return self + (-other)
def __rsub__(self, other):
return -self + other
@_check_other_SISO
def __mul__(self, other):
if isinstance(other, Series):
arg_list = list(other.args)
return Series(self, *arg_list)
return Series(self, other)
def __neg__(self):
return Series(TransferFunction(-1, 1, self.var), self)
def to_expr(self):
"""Returns the equivalent ``Expr`` object."""
return Add(*(arg.to_expr() for arg in self.args), evaluate=False)
@property
def is_proper(self):
"""
Returns True if degree of the numerator polynomial of the resultant transfer
function is less than or equal to degree of the denominator polynomial of
the same, else False.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Parallel
>>> tf1 = TransferFunction(b*s**2 + p**2 - a*p + s, b - p**2, s)
>>> tf2 = TransferFunction(p**2 - 4*p, p**3 + 3*s + 2, s)
>>> tf3 = TransferFunction(s, s**2 + s + 1, s)
>>> P1 = Parallel(-tf2, tf1)
>>> P1.is_proper
False
>>> P2 = Parallel(tf2, tf3)
>>> P2.is_proper
True
"""
return self.doit().is_proper
@property
def is_strictly_proper(self):
"""
Returns True if degree of the numerator polynomial of the resultant transfer
function is strictly less than degree of the denominator polynomial of
the same, else False.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Parallel
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)
>>> tf3 = TransferFunction(s, s**2 + s + 1, s)
>>> P1 = Parallel(tf1, tf2)
>>> P1.is_strictly_proper
False
>>> P2 = Parallel(tf2, tf3)
>>> P2.is_strictly_proper
True
"""
return self.doit().is_strictly_proper
@property
def is_biproper(self):
"""
Returns True if degree of the numerator polynomial of the resultant transfer
function is equal to degree of the denominator polynomial of
the same, else False.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, Parallel
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(p**2, p + s, s)
>>> tf3 = TransferFunction(s, s**2 + s + 1, s)
>>> P1 = Parallel(tf1, -tf2)
>>> P1.is_biproper
True
>>> P2 = Parallel(tf2, tf3)
>>> P2.is_biproper
False
"""
return self.doit().is_biproper
class MIMOParallel(MIMOLinearTimeInvariant):
r"""
A class for representing a parallel configuration of MIMO systems.
Parameters
==========
args : MIMOLinearTimeInvariant
MIMO Systems in a parallel arrangement.
evaluate : Boolean, Keyword
When passed ``True``, returns the equivalent
``MIMOParallel(*args).doit()``. Set to ``False`` by default.
Raises
======
ValueError
When no argument is passed.
``var`` attribute is not same for every system.
All MIMO systems passed do not have same shape.
TypeError
Any of the passed ``*args`` has unsupported type
A combination of SISO and MIMO systems is
passed. There should be homogeneity in the
type of systems passed, MIMO in this case.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunctionMatrix, MIMOParallel
>>> from sympy import Matrix, pprint
>>> expr_1 = 1/s
>>> expr_2 = s/(s**2-1)
>>> expr_3 = (2 + s)/(s**2 - 1)
>>> expr_4 = 5
>>> tfm_a = TransferFunctionMatrix.from_Matrix(Matrix([[expr_1, expr_2], [expr_3, expr_4]]), s)
>>> tfm_b = TransferFunctionMatrix.from_Matrix(Matrix([[expr_2, expr_1], [expr_4, expr_3]]), s)
>>> tfm_c = TransferFunctionMatrix.from_Matrix(Matrix([[expr_3, expr_4], [expr_1, expr_2]]), s)
>>> MIMOParallel(tfm_a, tfm_b, tfm_c)
MIMOParallel(TransferFunctionMatrix(((TransferFunction(1, s, s), TransferFunction(s, s**2 - 1, s)), (TransferFunction(s + 2, s**2 - 1, s), TransferFunction(5, 1, s)))), TransferFunctionMatrix(((TransferFunction(s, s**2 - 1, s), TransferFunction(1, s, s)), (TransferFunction(5, 1, s), TransferFunction(s + 2, s**2 - 1, s)))), TransferFunctionMatrix(((TransferFunction(s + 2, s**2 - 1, s), TransferFunction(5, 1, s)), (TransferFunction(1, s, s), TransferFunction(s, s**2 - 1, s)))))
>>> pprint(_, use_unicode=False) # For Better Visualization
[ 1 s ] [ s 1 ] [s + 2 5 ]
[ - ------] [------ - ] [------ - ]
[ s 2 ] [ 2 s ] [ 2 1 ]
[ s - 1] [s - 1 ] [s - 1 ]
[ ] + [ ] + [ ]
[s + 2 5 ] [ 5 s + 2 ] [ 1 s ]
[------ - ] [ - ------] [ - ------]
[ 2 1 ] [ 1 2 ] [ s 2 ]
[s - 1 ]{t} [ s - 1]{t} [ s - 1]{t}
>>> MIMOParallel(tfm_a, tfm_b, tfm_c).doit()
TransferFunctionMatrix(((TransferFunction(s**2 + s*(2*s + 2) - 1, s*(s**2 - 1), s), TransferFunction(2*s**2 + 5*s*(s**2 - 1) - 1, s*(s**2 - 1), s)), (TransferFunction(s**2 + s*(s + 2) + 5*s*(s**2 - 1) - 1, s*(s**2 - 1), s), TransferFunction(5*s**2 + 2*s - 3, s**2 - 1, s))))
>>> pprint(_, use_unicode=False)
[ 2 2 / 2 \ ]
[ s + s*(2*s + 2) - 1 2*s + 5*s*\s - 1/ - 1]
[ -------------------- -----------------------]
[ / 2 \ / 2 \ ]
[ s*\s - 1/ s*\s - 1/ ]
[ ]
[ 2 / 2 \ 2 ]
[s + s*(s + 2) + 5*s*\s - 1/ - 1 5*s + 2*s - 3 ]
[--------------------------------- -------------- ]
[ / 2 \ 2 ]
[ s*\s - 1/ s - 1 ]{t}
Notes
=====
All the transfer function matrices should use the same complex variable
``var`` of the Laplace transform.
See Also
========
Parallel, MIMOSeries
"""
def __new__(cls, *args, evaluate=False):
args = _flatten_args(args, MIMOParallel)
cls._check_args(args)
if any(arg.shape != args[0].shape for arg in args):
raise TypeError("Shape of all the args is not equal.")
obj = super().__new__(cls, *args)
return obj.doit() if evaluate else obj
@property
def var(self):
"""
Returns the complex variable used by all the systems.
Examples
========
>>> from sympy.abc import p
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOParallel
>>> G1 = TransferFunction(p**2 + 2*p + 4, p - 6, p)
>>> G2 = TransferFunction(p, 4 - p, p)
>>> G3 = TransferFunction(0, p**4 - 1, p)
>>> G4 = TransferFunction(p**2, p**2 - 1, p)
>>> tfm_a = TransferFunctionMatrix([[G1, G2], [G3, G4]])
>>> tfm_b = TransferFunctionMatrix([[G2, G1], [G4, G3]])
>>> MIMOParallel(tfm_a, tfm_b).var
p
"""
return self.args[0].var
@property
def num_inputs(self):
"""Returns the number of input signals of the parallel system."""
return self.args[0].num_inputs
@property
def num_outputs(self):
"""Returns the number of output signals of the parallel system."""
return self.args[0].num_outputs
@property
def shape(self):
"""Returns the shape of the equivalent MIMO system."""
return self.num_outputs, self.num_inputs
def doit(self, **hints):
"""
Returns the resultant transfer function matrix obtained after evaluating
the MIMO systems arranged in a parallel configuration.
Examples
========
>>> from sympy.abc import s, p, a, b
>>> from sympy.physics.control.lti import TransferFunction, MIMOParallel, TransferFunctionMatrix
>>> tf1 = TransferFunction(a*p**2 + b*s, s - p, s)
>>> tf2 = TransferFunction(s**3 - 2, s**4 + 5*s + 6, s)
>>> tfm_1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
>>> tfm_2 = TransferFunctionMatrix([[tf2, tf1], [tf1, tf2]])
>>> MIMOParallel(tfm_1, tfm_2).doit()
TransferFunctionMatrix(((TransferFunction((-p + s)*(s**3 - 2) + (a*p**2 + b*s)*(s**4 + 5*s + 6), (-p + s)*(s**4 + 5*s + 6), s), TransferFunction((-p + s)*(s**3 - 2) + (a*p**2 + b*s)*(s**4 + 5*s + 6), (-p + s)*(s**4 + 5*s + 6), s)), (TransferFunction((-p + s)*(s**3 - 2) + (a*p**2 + b*s)*(s**4 + 5*s + 6), (-p + s)*(s**4 + 5*s + 6), s), TransferFunction((-p + s)*(s**3 - 2) + (a*p**2 + b*s)*(s**4 + 5*s + 6), (-p + s)*(s**4 + 5*s + 6), s))))
"""
_arg = (arg.doit()._expr_mat for arg in self.args)
res = MatAdd(*_arg, evaluate=True)
return TransferFunctionMatrix.from_Matrix(res, self.var)
def _eval_rewrite_as_TransferFunctionMatrix(self, *args, **kwargs):
return self.doit()
@_check_other_MIMO
def __add__(self, other):
self_arg_list = list(self.args)
return MIMOParallel(*self_arg_list, other)
__radd__ = __add__
@_check_other_MIMO
def __sub__(self, other):
return self + (-other)
def __rsub__(self, other):
return -self + other
@_check_other_MIMO
def __mul__(self, other):
if isinstance(other, MIMOSeries):
arg_list = list(other.args)
return MIMOSeries(*arg_list, self)
return MIMOSeries(other, self)
def __neg__(self):
arg_list = [-arg for arg in list(self.args)]
return MIMOParallel(*arg_list)
class Feedback(TransferFunction):
r"""
A class for representing closed-loop feedback interconnection between two
SISO input/output systems.
The first argument, ``sys1``, is the feedforward part of the closed-loop
system or in simple words, the dynamical model representing the process
to be controlled. The second argument, ``sys2``, is the feedback system
and controls the fed back signal to ``sys1``. Both ``sys1`` and ``sys2``
can either be ``Series`` or ``TransferFunction`` objects.
Parameters
==========
sys1 : Series, TransferFunction
The feedforward path system.
sys2 : Series, TransferFunction, optional
The feedback path system (often a feedback controller).
It is the model sitting on the feedback path.
If not specified explicitly, the sys2 is
assumed to be unit (1.0) transfer function.
sign : int, optional
The sign of feedback. Can either be ``1``
(for positive feedback) or ``-1`` (for negative feedback).
Default value is `-1`.
Raises
======
ValueError
When ``sys1`` and ``sys2`` are not using the
same complex variable of the Laplace transform.
When a combination of ``sys1`` and ``sys2`` yields
zero denominator.
TypeError
When either ``sys1`` or ``sys2`` is not a ``Series`` or a
``TransferFunction`` object.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunction, Feedback
>>> plant = TransferFunction(3*s**2 + 7*s - 3, s**2 - 4*s + 2, s)
>>> controller = TransferFunction(5*s - 10, s + 7, s)
>>> F1 = Feedback(plant, controller)
>>> F1
Feedback(TransferFunction(3*s**2 + 7*s - 3, s**2 - 4*s + 2, s), TransferFunction(5*s - 10, s + 7, s), -1)
>>> F1.var
s
>>> F1.args
(TransferFunction(3*s**2 + 7*s - 3, s**2 - 4*s + 2, s), TransferFunction(5*s - 10, s + 7, s), -1)
You can get the feedforward and feedback path systems by using ``.sys1`` and ``.sys2`` respectively.
>>> F1.sys1
TransferFunction(3*s**2 + 7*s - 3, s**2 - 4*s + 2, s)
>>> F1.sys2
TransferFunction(5*s - 10, s + 7, s)
You can get the resultant closed loop transfer function obtained by negative feedback
interconnection using ``.doit()`` method.
>>> F1.doit()
TransferFunction((s + 7)*(s**2 - 4*s + 2)*(3*s**2 + 7*s - 3), ((s + 7)*(s**2 - 4*s + 2) + (5*s - 10)*(3*s**2 + 7*s - 3))*(s**2 - 4*s + 2), s)
>>> G = TransferFunction(2*s**2 + 5*s + 1, s**2 + 2*s + 3, s)
>>> C = TransferFunction(5*s + 10, s + 10, s)
>>> F2 = Feedback(G*C, TransferFunction(1, 1, s))
>>> F2.doit()
TransferFunction((s + 10)*(5*s + 10)*(s**2 + 2*s + 3)*(2*s**2 + 5*s + 1), (s + 10)*((s + 10)*(s**2 + 2*s + 3) + (5*s + 10)*(2*s**2 + 5*s + 1))*(s**2 + 2*s + 3), s)
To negate a ``Feedback`` object, the ``-`` operator can be prepended:
>>> -F1
Feedback(TransferFunction(-3*s**2 - 7*s + 3, s**2 - 4*s + 2, s), TransferFunction(10 - 5*s, s + 7, s), -1)
>>> -F2
Feedback(Series(TransferFunction(-1, 1, s), TransferFunction(2*s**2 + 5*s + 1, s**2 + 2*s + 3, s), TransferFunction(5*s + 10, s + 10, s)), TransferFunction(-1, 1, s), -1)
See Also
========
MIMOFeedback, Series, Parallel
"""
def __new__(cls, sys1, sys2=None, sign=-1):
if not sys2:
sys2 = TransferFunction(1, 1, sys1.var)
if not (isinstance(sys1, (TransferFunction, Series, Feedback))
and isinstance(sys2, (TransferFunction, Series, Feedback))):
raise TypeError("Unsupported type for `sys1` or `sys2` of Feedback.")
if sign not in [-1, 1]:
raise ValueError(filldedent("""
Unsupported type for feedback. `sign` arg should
either be 1 (positive feedback loop) or -1
(negative feedback loop)."""))
if Mul(sys1.to_expr(), sys2.to_expr()).simplify() == sign:
raise ValueError("The equivalent system will have zero denominator.")
if sys1.var != sys2.var:
raise ValueError(filldedent("""
Both `sys1` and `sys2` should be using the
same complex variable."""))
return super(TransferFunction, cls).__new__(cls, sys1, sys2, _sympify(sign))
@property
def sys1(self):
"""
Returns the feedforward system of the feedback interconnection.
Examples
========
>>> from sympy.abc import s, p
>>> from sympy.physics.control.lti import TransferFunction, Feedback
>>> plant = TransferFunction(3*s**2 + 7*s - 3, s**2 - 4*s + 2, s)
>>> controller = TransferFunction(5*s - 10, s + 7, s)
>>> F1 = Feedback(plant, controller)
>>> F1.sys1
TransferFunction(3*s**2 + 7*s - 3, s**2 - 4*s + 2, s)
>>> G = TransferFunction(2*s**2 + 5*s + 1, p**2 + 2*p + 3, p)
>>> C = TransferFunction(5*p + 10, p + 10, p)
>>> P = TransferFunction(1 - s, p + 2, p)
>>> F2 = Feedback(TransferFunction(1, 1, p), G*C*P)
>>> F2.sys1
TransferFunction(1, 1, p)
"""
return self.args[0]
@property
def sys2(self):
"""
Returns the feedback controller of the feedback interconnection.
Examples
========
>>> from sympy.abc import s, p
>>> from sympy.physics.control.lti import TransferFunction, Feedback
>>> plant = TransferFunction(3*s**2 + 7*s - 3, s**2 - 4*s + 2, s)
>>> controller = TransferFunction(5*s - 10, s + 7, s)
>>> F1 = Feedback(plant, controller)
>>> F1.sys2
TransferFunction(5*s - 10, s + 7, s)
>>> G = TransferFunction(2*s**2 + 5*s + 1, p**2 + 2*p + 3, p)
>>> C = TransferFunction(5*p + 10, p + 10, p)
>>> P = TransferFunction(1 - s, p + 2, p)
>>> F2 = Feedback(TransferFunction(1, 1, p), G*C*P)
>>> F2.sys2
Series(TransferFunction(2*s**2 + 5*s + 1, p**2 + 2*p + 3, p), TransferFunction(5*p + 10, p + 10, p), TransferFunction(1 - s, p + 2, p))
"""
return self.args[1]
@property
def var(self):
"""
Returns the complex variable of the Laplace transform used by all
the transfer functions involved in the feedback interconnection.
Examples
========
>>> from sympy.abc import s, p
>>> from sympy.physics.control.lti import TransferFunction, Feedback
>>> plant = TransferFunction(3*s**2 + 7*s - 3, s**2 - 4*s + 2, s)
>>> controller = TransferFunction(5*s - 10, s + 7, s)
>>> F1 = Feedback(plant, controller)
>>> F1.var
s
>>> G = TransferFunction(2*s**2 + 5*s + 1, p**2 + 2*p + 3, p)
>>> C = TransferFunction(5*p + 10, p + 10, p)
>>> P = TransferFunction(1 - s, p + 2, p)
>>> F2 = Feedback(TransferFunction(1, 1, p), G*C*P)
>>> F2.var
p
"""
return self.sys1.var
@property
def sign(self):
"""
Returns the type of MIMO Feedback model. ``1``
for Positive and ``-1`` for Negative.
"""
return self.args[2]
@property
def num(self):
"""
Returns the numerator of the closed loop feedback system.
"""
return self.sys1
@property
def den(self):
"""
Returns the denominator of the closed loop feedback model.
"""
unit = TransferFunction(1, 1, self.var)
arg_list = list(self.sys1.args) if isinstance(self.sys1, Series) else [self.sys1]
if self.sign == 1:
return Parallel(unit, -Series(self.sys2, *arg_list))
return Parallel(unit, Series(self.sys2, *arg_list))
@property
def sensitivity(self):
"""
Returns the sensitivity function of the feedback loop.
Sensitivity of a Feedback system is the ratio
of change in the open loop gain to the change in
the closed loop gain.
.. note::
This method would not return the complementary
sensitivity function.
Examples
========
>>> from sympy.abc import p
>>> from sympy.physics.control.lti import TransferFunction, Feedback
>>> C = TransferFunction(5*p + 10, p + 10, p)
>>> P = TransferFunction(1 - p, p + 2, p)
>>> F_1 = Feedback(P, C)
>>> F_1.sensitivity
1/((1 - p)*(5*p + 10)/((p + 2)*(p + 10)) + 1)
"""
return 1/(1 - self.sign*self.sys1.to_expr()*self.sys2.to_expr())
def doit(self, cancel=False, expand=False, **hints):
"""
Returns the resultant transfer function obtained by the
feedback interconnection.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunction, Feedback
>>> plant = TransferFunction(3*s**2 + 7*s - 3, s**2 - 4*s + 2, s)
>>> controller = TransferFunction(5*s - 10, s + 7, s)
>>> F1 = Feedback(plant, controller)
>>> F1.doit()
TransferFunction((s + 7)*(s**2 - 4*s + 2)*(3*s**2 + 7*s - 3), ((s + 7)*(s**2 - 4*s + 2) + (5*s - 10)*(3*s**2 + 7*s - 3))*(s**2 - 4*s + 2), s)
>>> G = TransferFunction(2*s**2 + 5*s + 1, s**2 + 2*s + 3, s)
>>> F2 = Feedback(G, TransferFunction(1, 1, s))
>>> F2.doit()
TransferFunction((s**2 + 2*s + 3)*(2*s**2 + 5*s + 1), (s**2 + 2*s + 3)*(3*s**2 + 7*s + 4), s)
Use kwarg ``expand=True`` to expand the resultant transfer function.
Use ``cancel=True`` to cancel out the common terms in numerator and
denominator.
>>> F2.doit(cancel=True, expand=True)
TransferFunction(2*s**2 + 5*s + 1, 3*s**2 + 7*s + 4, s)
>>> F2.doit(expand=True)
TransferFunction(2*s**4 + 9*s**3 + 17*s**2 + 17*s + 3, 3*s**4 + 13*s**3 + 27*s**2 + 29*s + 12, s)
"""
arg_list = list(self.sys1.args) if isinstance(self.sys1, Series) else [self.sys1]
# F_n and F_d are resultant TFs of num and den of Feedback.
F_n, unit = self.sys1.doit(), TransferFunction(1, 1, self.sys1.var)
if self.sign == -1:
F_d = Parallel(unit, Series(self.sys2, *arg_list)).doit()
else:
F_d = Parallel(unit, -Series(self.sys2, *arg_list)).doit()
_resultant_tf = TransferFunction(F_n.num * F_d.den, F_n.den * F_d.num, F_n.var)
if cancel:
_resultant_tf = _resultant_tf.simplify()
if expand:
_resultant_tf = _resultant_tf.expand()
return _resultant_tf
def _eval_rewrite_as_TransferFunction(self, num, den, sign, **kwargs):
return self.doit()
def to_expr(self):
"""
Converts a ``Feedback`` object to SymPy Expr.
Examples
========
>>> from sympy.abc import s, a, b
>>> from sympy.physics.control.lti import TransferFunction, Feedback
>>> from sympy import Expr
>>> tf1 = TransferFunction(a+s, 1, s)
>>> tf2 = TransferFunction(b+s, 1, s)
>>> fd1 = Feedback(tf1, tf2)
>>> fd1.to_expr()
(a + s)/((a + s)*(b + s) + 1)
>>> isinstance(_, Expr)
True
"""
return self.doit().to_expr()
def __neg__(self):
return Feedback(-self.sys1, -self.sys2, self.sign)
def _is_invertible(a, b, sign):
"""
Checks whether a given pair of MIMO
systems passed is invertible or not.
"""
_mat = eye(a.num_outputs) - sign*(a.doit()._expr_mat)*(b.doit()._expr_mat)
_det = _mat.det()
return _det != 0
class MIMOFeedback(MIMOLinearTimeInvariant):
r"""
A class for representing closed-loop feedback interconnection between two
MIMO input/output systems.
Parameters
==========
sys1 : MIMOSeries, TransferFunctionMatrix
The MIMO system placed on the feedforward path.
sys2 : MIMOSeries, TransferFunctionMatrix
The system placed on the feedback path
(often a feedback controller).
sign : int, optional
The sign of feedback. Can either be ``1``
(for positive feedback) or ``-1`` (for negative feedback).
Default value is `-1`.
Raises
======
ValueError
When ``sys1`` and ``sys2`` are not using the
same complex variable of the Laplace transform.
Forward path model should have an equal number of inputs/outputs
to the feedback path outputs/inputs.
When product of ``sys1`` and ``sys2`` is not a square matrix.
When the equivalent MIMO system is not invertible.
TypeError
When either ``sys1`` or ``sys2`` is not a ``MIMOSeries`` or a
``TransferFunctionMatrix`` object.
Examples
========
>>> from sympy import Matrix, pprint
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunctionMatrix, MIMOFeedback
>>> plant_mat = Matrix([[1, 1/s], [0, 1]])
>>> controller_mat = Matrix([[10, 0], [0, 10]]) # Constant Gain
>>> plant = TransferFunctionMatrix.from_Matrix(plant_mat, s)
>>> controller = TransferFunctionMatrix.from_Matrix(controller_mat, s)
>>> feedback = MIMOFeedback(plant, controller) # Negative Feedback (default)
>>> pprint(feedback, use_unicode=False)
/ [1 1] [10 0 ] \-1 [1 1]
| [- -] [-- - ] | [- -]
| [1 s] [1 1 ] | [1 s]
|I + [ ] *[ ] | * [ ]
| [0 1] [0 10] | [0 1]
| [- -] [- --] | [- -]
\ [1 1]{t} [1 1 ]{t}/ [1 1]{t}
To get the equivalent system matrix, use either ``doit`` or ``rewrite`` method.
>>> pprint(feedback.doit(), use_unicode=False)
[1 1 ]
[-- -----]
[11 121*s]
[ ]
[0 1 ]
[- -- ]
[1 11 ]{t}
To negate the ``MIMOFeedback`` object, use ``-`` operator.
>>> neg_feedback = -feedback
>>> pprint(neg_feedback.doit(), use_unicode=False)
[-1 -1 ]
[--- -----]
[11 121*s]
[ ]
[ 0 -1 ]
[ - --- ]
[ 1 11 ]{t}
See Also
========
Feedback, MIMOSeries, MIMOParallel
"""
def __new__(cls, sys1, sys2, sign=-1):
if not (isinstance(sys1, (TransferFunctionMatrix, MIMOSeries))
and isinstance(sys2, (TransferFunctionMatrix, MIMOSeries))):
raise TypeError("Unsupported type for `sys1` or `sys2` of MIMO Feedback.")
if sys1.num_inputs != sys2.num_outputs or \
sys1.num_outputs != sys2.num_inputs:
raise ValueError(filldedent("""
Product of `sys1` and `sys2` must
yield a square matrix."""))
if sign not in (-1, 1):
raise ValueError(filldedent("""
Unsupported type for feedback. `sign` arg should
either be 1 (positive feedback loop) or -1
(negative feedback loop)."""))
if not _is_invertible(sys1, sys2, sign):
raise ValueError("Non-Invertible system inputted.")
if sys1.var != sys2.var:
raise ValueError(filldedent("""
Both `sys1` and `sys2` should be using the
same complex variable."""))
return super().__new__(cls, sys1, sys2, _sympify(sign))
@property
def sys1(self):
r"""
Returns the system placed on the feedforward path of the MIMO feedback interconnection.
Examples
========
>>> from sympy import pprint
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
>>> tf1 = TransferFunction(s**2 + s + 1, s**2 - s + 1, s)
>>> tf2 = TransferFunction(1, s, s)
>>> tf3 = TransferFunction(1, 1, s)
>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
>>> sys2 = TransferFunctionMatrix([[tf3, tf3], [tf3, tf2]])
>>> F_1 = MIMOFeedback(sys1, sys2, 1)
>>> F_1.sys1
TransferFunctionMatrix(((TransferFunction(s**2 + s + 1, s**2 - s + 1, s), TransferFunction(1, s, s)), (TransferFunction(1, s, s), TransferFunction(s**2 + s + 1, s**2 - s + 1, s))))
>>> pprint(_, use_unicode=False)
[ 2 ]
[s + s + 1 1 ]
[---------- - ]
[ 2 s ]
[s - s + 1 ]
[ ]
[ 2 ]
[ 1 s + s + 1]
[ - ----------]
[ s 2 ]
[ s - s + 1]{t}
"""
return self.args[0]
@property
def sys2(self):
r"""
Returns the feedback controller of the MIMO feedback interconnection.
Examples
========
>>> from sympy import pprint
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
>>> tf1 = TransferFunction(s**2, s**3 - s + 1, s)
>>> tf2 = TransferFunction(1, s, s)
>>> tf3 = TransferFunction(1, 1, s)
>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
>>> sys2 = TransferFunctionMatrix([[tf1, tf3], [tf3, tf2]])
>>> F_1 = MIMOFeedback(sys1, sys2)
>>> F_1.sys2
TransferFunctionMatrix(((TransferFunction(s**2, s**3 - s + 1, s), TransferFunction(1, 1, s)), (TransferFunction(1, 1, s), TransferFunction(1, s, s))))
>>> pprint(_, use_unicode=False)
[ 2 ]
[ s 1]
[---------- -]
[ 3 1]
[s - s + 1 ]
[ ]
[ 1 1]
[ - -]
[ 1 s]{t}
"""
return self.args[1]
@property
def var(self):
r"""
Returns the complex variable of the Laplace transform used by all
the transfer functions involved in the MIMO feedback loop.
Examples
========
>>> from sympy.abc import p
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
>>> tf1 = TransferFunction(p, 1 - p, p)
>>> tf2 = TransferFunction(1, p, p)
>>> tf3 = TransferFunction(1, 1, p)
>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
>>> sys2 = TransferFunctionMatrix([[tf1, tf3], [tf3, tf2]])
>>> F_1 = MIMOFeedback(sys1, sys2, 1) # Positive feedback
>>> F_1.var
p
"""
return self.sys1.var
@property
def sign(self):
r"""
Returns the type of feedback interconnection of two models. ``1``
for Positive and ``-1`` for Negative.
"""
return self.args[2]
@property
def sensitivity(self):
r"""
Returns the sensitivity function matrix of the feedback loop.
Sensitivity of a closed-loop system is the ratio of change
in the open loop gain to the change in the closed loop gain.
.. note::
This method would not return the complementary
sensitivity function.
Examples
========
>>> from sympy import pprint
>>> from sympy.abc import p
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
>>> tf1 = TransferFunction(p, 1 - p, p)
>>> tf2 = TransferFunction(1, p, p)
>>> tf3 = TransferFunction(1, 1, p)
>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
>>> sys2 = TransferFunctionMatrix([[tf1, tf3], [tf3, tf2]])
>>> F_1 = MIMOFeedback(sys1, sys2, 1) # Positive feedback
>>> F_2 = MIMOFeedback(sys1, sys2) # Negative feedback
>>> pprint(F_1.sensitivity, use_unicode=False)
[ 4 3 2 5 4 2 ]
[- p + 3*p - 4*p + 3*p - 1 p - 2*p + 3*p - 3*p + 1 ]
[---------------------------- -----------------------------]
[ 4 3 2 5 4 3 2 ]
[ p + 3*p - 8*p + 8*p - 3 p + 3*p - 8*p + 8*p - 3*p]
[ ]
[ 4 3 2 3 2 ]
[ p - p - p + p 3*p - 6*p + 4*p - 1 ]
[ -------------------------- -------------------------- ]
[ 4 3 2 4 3 2 ]
[ p + 3*p - 8*p + 8*p - 3 p + 3*p - 8*p + 8*p - 3 ]
>>> pprint(F_2.sensitivity, use_unicode=False)
[ 4 3 2 5 4 2 ]
[p - 3*p + 2*p + p - 1 p - 2*p + 3*p - 3*p + 1]
[------------------------ --------------------------]
[ 4 3 5 4 2 ]
[ p - 3*p + 2*p - 1 p - 3*p + 2*p - p ]
[ ]
[ 4 3 2 4 3 ]
[ p - p - p + p 2*p - 3*p + 2*p - 1 ]
[ ------------------- --------------------- ]
[ 4 3 4 3 ]
[ p - 3*p + 2*p - 1 p - 3*p + 2*p - 1 ]
"""
_sys1_mat = self.sys1.doit()._expr_mat
_sys2_mat = self.sys2.doit()._expr_mat
return (eye(self.sys1.num_inputs) - \
self.sign*_sys1_mat*_sys2_mat).inv()
def doit(self, cancel=True, expand=False, **hints):
r"""
Returns the resultant transfer function matrix obtained by the
feedback interconnection.
Examples
========
>>> from sympy import pprint
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
>>> tf1 = TransferFunction(s, 1 - s, s)
>>> tf2 = TransferFunction(1, s, s)
>>> tf3 = TransferFunction(5, 1, s)
>>> tf4 = TransferFunction(s - 1, s, s)
>>> tf5 = TransferFunction(0, 1, s)
>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf3, tf4]])
>>> sys2 = TransferFunctionMatrix([[tf3, tf5], [tf5, tf5]])
>>> F_1 = MIMOFeedback(sys1, sys2, 1)
>>> pprint(F_1, use_unicode=False)
/ [ s 1 ] [5 0] \-1 [ s 1 ]
| [----- - ] [- -] | [----- - ]
| [1 - s s ] [1 1] | [1 - s s ]
|I - [ ] *[ ] | * [ ]
| [ 5 s - 1] [0 0] | [ 5 s - 1]
| [ - -----] [- -] | [ - -----]
\ [ 1 s ]{t} [1 1]{t}/ [ 1 s ]{t}
>>> pprint(F_1.doit(), use_unicode=False)
[ -s s - 1 ]
[------- ----------- ]
[6*s - 1 s*(6*s - 1) ]
[ ]
[5*s - 5 (s - 1)*(6*s + 24)]
[------- ------------------]
[6*s - 1 s*(6*s - 1) ]{t}
If the user wants the resultant ``TransferFunctionMatrix`` object without
canceling the common factors then the ``cancel`` kwarg should be passed ``False``.
>>> pprint(F_1.doit(cancel=False), use_unicode=False)
[ s*(s - 1) s - 1 ]
[ ----------------- ----------- ]
[ (1 - s)*(6*s - 1) s*(6*s - 1) ]
[ ]
[s*(25*s - 25) + 5*(1 - s)*(6*s - 1) s*(s - 1)*(6*s - 1) + s*(25*s - 25)]
[----------------------------------- -----------------------------------]
[ (1 - s)*(6*s - 1) 2 ]
[ s *(6*s - 1) ]{t}
If the user wants the expanded form of the resultant transfer function matrix,
the ``expand`` kwarg should be passed as ``True``.
>>> pprint(F_1.doit(expand=True), use_unicode=False)
[ -s s - 1 ]
[------- -------- ]
[6*s - 1 2 ]
[ 6*s - s ]
[ ]
[ 2 ]
[5*s - 5 6*s + 18*s - 24]
[------- ----------------]
[6*s - 1 2 ]
[ 6*s - s ]{t}
"""
_mat = self.sensitivity * self.sys1.doit()._expr_mat
_resultant_tfm = _to_TFM(_mat, self.var)
if cancel:
_resultant_tfm = _resultant_tfm.simplify()
if expand:
_resultant_tfm = _resultant_tfm.expand()
return _resultant_tfm
def _eval_rewrite_as_TransferFunctionMatrix(self, sys1, sys2, sign, **kwargs):
return self.doit()
def __neg__(self):
return MIMOFeedback(-self.sys1, -self.sys2, self.sign)
def _to_TFM(mat, var):
"""Private method to convert ImmutableMatrix to TransferFunctionMatrix efficiently"""
to_tf = lambda expr: TransferFunction.from_rational_expression(expr, var)
arg = [[to_tf(expr) for expr in row] for row in mat.tolist()]
return TransferFunctionMatrix(arg)
class TransferFunctionMatrix(MIMOLinearTimeInvariant):
r"""
A class for representing the MIMO (multiple-input and multiple-output)
generalization of the SISO (single-input and single-output) transfer function.
It is a matrix of transfer functions (``TransferFunction``, SISO-``Series`` or SISO-``Parallel``).
There is only one argument, ``arg`` which is also the compulsory argument.
``arg`` is expected to be strictly of the type list of lists
which holds the transfer functions or reducible to transfer functions.
Parameters
==========
arg : Nested ``List`` (strictly).
Users are expected to input a nested list of ``TransferFunction``, ``Series``
and/or ``Parallel`` objects.
Examples
========
.. note::
``pprint()`` can be used for better visualization of ``TransferFunctionMatrix`` objects.
>>> from sympy.abc import s, p, a
>>> from sympy import pprint
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, Series, Parallel
>>> tf_1 = TransferFunction(s + a, s**2 + s + 1, s)
>>> tf_2 = TransferFunction(p**4 - 3*p + 2, s + p, s)
>>> tf_3 = TransferFunction(3, s + 2, s)
>>> tf_4 = TransferFunction(-a + p, 9*s - 9, s)
>>> tfm_1 = TransferFunctionMatrix([[tf_1], [tf_2], [tf_3]])
>>> tfm_1
TransferFunctionMatrix(((TransferFunction(a + s, s**2 + s + 1, s),), (TransferFunction(p**4 - 3*p + 2, p + s, s),), (TransferFunction(3, s + 2, s),)))
>>> tfm_1.var
s
>>> tfm_1.num_inputs
1
>>> tfm_1.num_outputs
3
>>> tfm_1.shape
(3, 1)
>>> tfm_1.args
(((TransferFunction(a + s, s**2 + s + 1, s),), (TransferFunction(p**4 - 3*p + 2, p + s, s),), (TransferFunction(3, s + 2, s),)),)
>>> tfm_2 = TransferFunctionMatrix([[tf_1, -tf_3], [tf_2, -tf_1], [tf_3, -tf_2]])
>>> tfm_2
TransferFunctionMatrix(((TransferFunction(a + s, s**2 + s + 1, s), TransferFunction(-3, s + 2, s)), (TransferFunction(p**4 - 3*p + 2, p + s, s), TransferFunction(-a - s, s**2 + s + 1, s)), (TransferFunction(3, s + 2, s), TransferFunction(-p**4 + 3*p - 2, p + s, s))))
>>> pprint(tfm_2, use_unicode=False) # pretty-printing for better visualization
[ a + s -3 ]
[ ---------- ----- ]
[ 2 s + 2 ]
[ s + s + 1 ]
[ ]
[ 4 ]
[p - 3*p + 2 -a - s ]
[------------ ---------- ]
[ p + s 2 ]
[ s + s + 1 ]
[ ]
[ 4 ]
[ 3 - p + 3*p - 2]
[ ----- --------------]
[ s + 2 p + s ]{t}
TransferFunctionMatrix can be transposed, if user wants to switch the input and output transfer functions
>>> tfm_2.transpose()
TransferFunctionMatrix(((TransferFunction(a + s, s**2 + s + 1, s), TransferFunction(p**4 - 3*p + 2, p + s, s), TransferFunction(3, s + 2, s)), (TransferFunction(-3, s + 2, s), TransferFunction(-a - s, s**2 + s + 1, s), TransferFunction(-p**4 + 3*p - 2, p + s, s))))
>>> pprint(_, use_unicode=False)
[ 4 ]
[ a + s p - 3*p + 2 3 ]
[---------- ------------ ----- ]
[ 2 p + s s + 2 ]
[s + s + 1 ]
[ ]
[ 4 ]
[ -3 -a - s - p + 3*p - 2]
[ ----- ---------- --------------]
[ s + 2 2 p + s ]
[ s + s + 1 ]{t}
>>> tf_5 = TransferFunction(5, s, s)
>>> tf_6 = TransferFunction(5*s, (2 + s**2), s)
>>> tf_7 = TransferFunction(5, (s*(2 + s**2)), s)
>>> tf_8 = TransferFunction(5, 1, s)
>>> tfm_3 = TransferFunctionMatrix([[tf_5, tf_6], [tf_7, tf_8]])
>>> tfm_3
TransferFunctionMatrix(((TransferFunction(5, s, s), TransferFunction(5*s, s**2 + 2, s)), (TransferFunction(5, s*(s**2 + 2), s), TransferFunction(5, 1, s))))
>>> pprint(tfm_3, use_unicode=False)
[ 5 5*s ]
[ - ------]
[ s 2 ]
[ s + 2]
[ ]
[ 5 5 ]
[---------- - ]
[ / 2 \ 1 ]
[s*\s + 2/ ]{t}
>>> tfm_3.var
s
>>> tfm_3.shape
(2, 2)
>>> tfm_3.num_outputs
2
>>> tfm_3.num_inputs
2
>>> tfm_3.args
(((TransferFunction(5, s, s), TransferFunction(5*s, s**2 + 2, s)), (TransferFunction(5, s*(s**2 + 2), s), TransferFunction(5, 1, s))),)
To access the ``TransferFunction`` at any index in the ``TransferFunctionMatrix``, use the index notation.
>>> tfm_3[1, 0] # gives the TransferFunction present at 2nd Row and 1st Col. Similar to that in Matrix classes
TransferFunction(5, s*(s**2 + 2), s)
>>> tfm_3[0, 0] # gives the TransferFunction present at 1st Row and 1st Col.
TransferFunction(5, s, s)
>>> tfm_3[:, 0] # gives the first column
TransferFunctionMatrix(((TransferFunction(5, s, s),), (TransferFunction(5, s*(s**2 + 2), s),)))
>>> pprint(_, use_unicode=False)
[ 5 ]
[ - ]
[ s ]
[ ]
[ 5 ]
[----------]
[ / 2 \]
[s*\s + 2/]{t}
>>> tfm_3[0, :] # gives the first row
TransferFunctionMatrix(((TransferFunction(5, s, s), TransferFunction(5*s, s**2 + 2, s)),))
>>> pprint(_, use_unicode=False)
[5 5*s ]
[- ------]
[s 2 ]
[ s + 2]{t}
To negate a transfer function matrix, ``-`` operator can be prepended:
>>> tfm_4 = TransferFunctionMatrix([[tf_2], [-tf_1], [tf_3]])
>>> -tfm_4
TransferFunctionMatrix(((TransferFunction(-p**4 + 3*p - 2, p + s, s),), (TransferFunction(a + s, s**2 + s + 1, s),), (TransferFunction(-3, s + 2, s),)))
>>> tfm_5 = TransferFunctionMatrix([[tf_1, tf_2], [tf_3, -tf_1]])
>>> -tfm_5
TransferFunctionMatrix(((TransferFunction(-a - s, s**2 + s + 1, s), TransferFunction(-p**4 + 3*p - 2, p + s, s)), (TransferFunction(-3, s + 2, s), TransferFunction(a + s, s**2 + s + 1, s))))
``subs()`` returns the ``TransferFunctionMatrix`` object with the value substituted in the expression. This will not
mutate your original ``TransferFunctionMatrix``.
>>> tfm_2.subs(p, 2) # substituting p everywhere in tfm_2 with 2.
TransferFunctionMatrix(((TransferFunction(a + s, s**2 + s + 1, s), TransferFunction(-3, s + 2, s)), (TransferFunction(12, s + 2, s), TransferFunction(-a - s, s**2 + s + 1, s)), (TransferFunction(3, s + 2, s), TransferFunction(-12, s + 2, s))))
>>> pprint(_, use_unicode=False)
[ a + s -3 ]
[---------- ----- ]
[ 2 s + 2 ]
[s + s + 1 ]
[ ]
[ 12 -a - s ]
[ ----- ----------]
[ s + 2 2 ]
[ s + s + 1]
[ ]
[ 3 -12 ]
[ ----- ----- ]
[ s + 2 s + 2 ]{t}
>>> pprint(tfm_2, use_unicode=False) # State of tfm_2 is unchanged after substitution
[ a + s -3 ]
[ ---------- ----- ]
[ 2 s + 2 ]
[ s + s + 1 ]
[ ]
[ 4 ]
[p - 3*p + 2 -a - s ]
[------------ ---------- ]
[ p + s 2 ]
[ s + s + 1 ]
[ ]
[ 4 ]
[ 3 - p + 3*p - 2]
[ ----- --------------]
[ s + 2 p + s ]{t}
``subs()`` also supports multiple substitutions.
>>> tfm_2.subs({p: 2, a: 1}) # substituting p with 2 and a with 1
TransferFunctionMatrix(((TransferFunction(s + 1, s**2 + s + 1, s), TransferFunction(-3, s + 2, s)), (TransferFunction(12, s + 2, s), TransferFunction(-s - 1, s**2 + s + 1, s)), (TransferFunction(3, s + 2, s), TransferFunction(-12, s + 2, s))))
>>> pprint(_, use_unicode=False)
[ s + 1 -3 ]
[---------- ----- ]
[ 2 s + 2 ]
[s + s + 1 ]
[ ]
[ 12 -s - 1 ]
[ ----- ----------]
[ s + 2 2 ]
[ s + s + 1]
[ ]
[ 3 -12 ]
[ ----- ----- ]
[ s + 2 s + 2 ]{t}
Users can reduce the ``Series`` and ``Parallel`` elements of the matrix to ``TransferFunction`` by using
``doit()``.
>>> tfm_6 = TransferFunctionMatrix([[Series(tf_3, tf_4), Parallel(tf_3, tf_4)]])
>>> tfm_6
TransferFunctionMatrix(((Series(TransferFunction(3, s + 2, s), TransferFunction(-a + p, 9*s - 9, s)), Parallel(TransferFunction(3, s + 2, s), TransferFunction(-a + p, 9*s - 9, s))),))
>>> pprint(tfm_6, use_unicode=False)
[-a + p 3 -a + p 3 ]
[-------*----- ------- + -----]
[9*s - 9 s + 2 9*s - 9 s + 2]{t}
>>> tfm_6.doit()
TransferFunctionMatrix(((TransferFunction(-3*a + 3*p, (s + 2)*(9*s - 9), s), TransferFunction(27*s + (-a + p)*(s + 2) - 27, (s + 2)*(9*s - 9), s)),))
>>> pprint(_, use_unicode=False)
[ -3*a + 3*p 27*s + (-a + p)*(s + 2) - 27]
[----------------- ----------------------------]
[(s + 2)*(9*s - 9) (s + 2)*(9*s - 9) ]{t}
>>> tf_9 = TransferFunction(1, s, s)
>>> tf_10 = TransferFunction(1, s**2, s)
>>> tfm_7 = TransferFunctionMatrix([[Series(tf_9, tf_10), tf_9], [tf_10, Parallel(tf_9, tf_10)]])
>>> tfm_7
TransferFunctionMatrix(((Series(TransferFunction(1, s, s), TransferFunction(1, s**2, s)), TransferFunction(1, s, s)), (TransferFunction(1, s**2, s), Parallel(TransferFunction(1, s, s), TransferFunction(1, s**2, s)))))
>>> pprint(tfm_7, use_unicode=False)
[ 1 1 ]
[---- - ]
[ 2 s ]
[s*s ]
[ ]
[ 1 1 1]
[ -- -- + -]
[ 2 2 s]
[ s s ]{t}
>>> tfm_7.doit()
TransferFunctionMatrix(((TransferFunction(1, s**3, s), TransferFunction(1, s, s)), (TransferFunction(1, s**2, s), TransferFunction(s**2 + s, s**3, s))))
>>> pprint(_, use_unicode=False)
[1 1 ]
[-- - ]
[ 3 s ]
[s ]
[ ]
[ 2 ]
[1 s + s]
[-- ------]
[ 2 3 ]
[s s ]{t}
Addition, subtraction, and multiplication of transfer function matrices can form
unevaluated ``Series`` or ``Parallel`` objects.
- For addition and subtraction:
All the transfer function matrices must have the same shape.
- For multiplication (C = A * B):
The number of inputs of the first transfer function matrix (A) must be equal to the
number of outputs of the second transfer function matrix (B).
Also, use pretty-printing (``pprint``) to analyse better.
>>> tfm_8 = TransferFunctionMatrix([[tf_3], [tf_2], [-tf_1]])
>>> tfm_9 = TransferFunctionMatrix([[-tf_3]])
>>> tfm_10 = TransferFunctionMatrix([[tf_1], [tf_2], [tf_4]])
>>> tfm_11 = TransferFunctionMatrix([[tf_4], [-tf_1]])
>>> tfm_12 = TransferFunctionMatrix([[tf_4, -tf_1, tf_3], [-tf_2, -tf_4, -tf_3]])
>>> tfm_8 + tfm_10
MIMOParallel(TransferFunctionMatrix(((TransferFunction(3, s + 2, s),), (TransferFunction(p**4 - 3*p + 2, p + s, s),), (TransferFunction(-a - s, s**2 + s + 1, s),))), TransferFunctionMatrix(((TransferFunction(a + s, s**2 + s + 1, s),), (TransferFunction(p**4 - 3*p + 2, p + s, s),), (TransferFunction(-a + p, 9*s - 9, s),))))
>>> pprint(_, use_unicode=False)
[ 3 ] [ a + s ]
[ ----- ] [ ---------- ]
[ s + 2 ] [ 2 ]
[ ] [ s + s + 1 ]
[ 4 ] [ ]
[p - 3*p + 2] [ 4 ]
[------------] + [p - 3*p + 2]
[ p + s ] [------------]
[ ] [ p + s ]
[ -a - s ] [ ]
[ ---------- ] [ -a + p ]
[ 2 ] [ ------- ]
[ s + s + 1 ]{t} [ 9*s - 9 ]{t}
>>> -tfm_10 - tfm_8
MIMOParallel(TransferFunctionMatrix(((TransferFunction(-a - s, s**2 + s + 1, s),), (TransferFunction(-p**4 + 3*p - 2, p + s, s),), (TransferFunction(a - p, 9*s - 9, s),))), TransferFunctionMatrix(((TransferFunction(-3, s + 2, s),), (TransferFunction(-p**4 + 3*p - 2, p + s, s),), (TransferFunction(a + s, s**2 + s + 1, s),))))
>>> pprint(_, use_unicode=False)
[ -a - s ] [ -3 ]
[ ---------- ] [ ----- ]
[ 2 ] [ s + 2 ]
[ s + s + 1 ] [ ]
[ ] [ 4 ]
[ 4 ] [- p + 3*p - 2]
[- p + 3*p - 2] + [--------------]
[--------------] [ p + s ]
[ p + s ] [ ]
[ ] [ a + s ]
[ a - p ] [ ---------- ]
[ ------- ] [ 2 ]
[ 9*s - 9 ]{t} [ s + s + 1 ]{t}
>>> tfm_12 * tfm_8
MIMOSeries(TransferFunctionMatrix(((TransferFunction(3, s + 2, s),), (TransferFunction(p**4 - 3*p + 2, p + s, s),), (TransferFunction(-a - s, s**2 + s + 1, s),))), TransferFunctionMatrix(((TransferFunction(-a + p, 9*s - 9, s), TransferFunction(-a - s, s**2 + s + 1, s), TransferFunction(3, s + 2, s)), (TransferFunction(-p**4 + 3*p - 2, p + s, s), TransferFunction(a - p, 9*s - 9, s), TransferFunction(-3, s + 2, s)))))
>>> pprint(_, use_unicode=False)
[ 3 ]
[ ----- ]
[ -a + p -a - s 3 ] [ s + 2 ]
[ ------- ---------- -----] [ ]
[ 9*s - 9 2 s + 2] [ 4 ]
[ s + s + 1 ] [p - 3*p + 2]
[ ] *[------------]
[ 4 ] [ p + s ]
[- p + 3*p - 2 a - p -3 ] [ ]
[-------------- ------- -----] [ -a - s ]
[ p + s 9*s - 9 s + 2]{t} [ ---------- ]
[ 2 ]
[ s + s + 1 ]{t}
>>> tfm_12 * tfm_8 * tfm_9
MIMOSeries(TransferFunctionMatrix(((TransferFunction(-3, s + 2, s),),)), TransferFunctionMatrix(((TransferFunction(3, s + 2, s),), (TransferFunction(p**4 - 3*p + 2, p + s, s),), (TransferFunction(-a - s, s**2 + s + 1, s),))), TransferFunctionMatrix(((TransferFunction(-a + p, 9*s - 9, s), TransferFunction(-a - s, s**2 + s + 1, s), TransferFunction(3, s + 2, s)), (TransferFunction(-p**4 + 3*p - 2, p + s, s), TransferFunction(a - p, 9*s - 9, s), TransferFunction(-3, s + 2, s)))))
>>> pprint(_, use_unicode=False)
[ 3 ]
[ ----- ]
[ -a + p -a - s 3 ] [ s + 2 ]
[ ------- ---------- -----] [ ]
[ 9*s - 9 2 s + 2] [ 4 ]
[ s + s + 1 ] [p - 3*p + 2] [ -3 ]
[ ] *[------------] *[-----]
[ 4 ] [ p + s ] [s + 2]{t}
[- p + 3*p - 2 a - p -3 ] [ ]
[-------------- ------- -----] [ -a - s ]
[ p + s 9*s - 9 s + 2]{t} [ ---------- ]
[ 2 ]
[ s + s + 1 ]{t}
>>> tfm_10 + tfm_8*tfm_9
MIMOParallel(TransferFunctionMatrix(((TransferFunction(a + s, s**2 + s + 1, s),), (TransferFunction(p**4 - 3*p + 2, p + s, s),), (TransferFunction(-a + p, 9*s - 9, s),))), MIMOSeries(TransferFunctionMatrix(((TransferFunction(-3, s + 2, s),),)), TransferFunctionMatrix(((TransferFunction(3, s + 2, s),), (TransferFunction(p**4 - 3*p + 2, p + s, s),), (TransferFunction(-a - s, s**2 + s + 1, s),)))))
>>> pprint(_, use_unicode=False)
[ a + s ] [ 3 ]
[ ---------- ] [ ----- ]
[ 2 ] [ s + 2 ]
[ s + s + 1 ] [ ]
[ ] [ 4 ]
[ 4 ] [p - 3*p + 2] [ -3 ]
[p - 3*p + 2] + [------------] *[-----]
[------------] [ p + s ] [s + 2]{t}
[ p + s ] [ ]
[ ] [ -a - s ]
[ -a + p ] [ ---------- ]
[ ------- ] [ 2 ]
[ 9*s - 9 ]{t} [ s + s + 1 ]{t}
These unevaluated ``Series`` or ``Parallel`` objects can convert into the
resultant transfer function matrix using ``.doit()`` method or by
``.rewrite(TransferFunctionMatrix)``.
>>> (-tfm_8 + tfm_10 + tfm_8*tfm_9).doit()
TransferFunctionMatrix(((TransferFunction((a + s)*(s + 2)**3 - 3*(s + 2)**2*(s**2 + s + 1) - 9*(s + 2)*(s**2 + s + 1), (s + 2)**3*(s**2 + s + 1), s),), (TransferFunction((p + s)*(-3*p**4 + 9*p - 6), (p + s)**2*(s + 2), s),), (TransferFunction((-a + p)*(s + 2)*(s**2 + s + 1)**2 + (a + s)*(s + 2)*(9*s - 9)*(s**2 + s + 1) + (3*a + 3*s)*(9*s - 9)*(s**2 + s + 1), (s + 2)*(9*s - 9)*(s**2 + s + 1)**2, s),)))
>>> (-tfm_12 * -tfm_8 * -tfm_9).rewrite(TransferFunctionMatrix)
TransferFunctionMatrix(((TransferFunction(3*(-3*a + 3*p)*(p + s)*(s + 2)*(s**2 + s + 1)**2 + 3*(-3*a - 3*s)*(p + s)*(s + 2)*(9*s - 9)*(s**2 + s + 1) + 3*(a + s)*(s + 2)**2*(9*s - 9)*(-p**4 + 3*p - 2)*(s**2 + s + 1), (p + s)*(s + 2)**3*(9*s - 9)*(s**2 + s + 1)**2, s),), (TransferFunction(3*(-a + p)*(p + s)*(s + 2)**2*(-p**4 + 3*p - 2)*(s**2 + s + 1) + 3*(3*a + 3*s)*(p + s)**2*(s + 2)*(9*s - 9) + 3*(p + s)*(s + 2)*(9*s - 9)*(-3*p**4 + 9*p - 6)*(s**2 + s + 1), (p + s)**2*(s + 2)**3*(9*s - 9)*(s**2 + s + 1), s),)))
See Also
========
TransferFunction, MIMOSeries, MIMOParallel, Feedback
"""
def __new__(cls, arg):
expr_mat_arg = []
try:
var = arg[0][0].var
except TypeError:
raise ValueError(filldedent("""
`arg` param in TransferFunctionMatrix should
strictly be a nested list containing TransferFunction
objects."""))
for row in arg:
temp = []
for element in row:
if not isinstance(element, SISOLinearTimeInvariant):
raise TypeError(filldedent("""
Each element is expected to be of
type `SISOLinearTimeInvariant`."""))
if var != element.var:
raise ValueError(filldedent("""
Conflicting value(s) found for `var`. All TransferFunction
instances in TransferFunctionMatrix should use the same
complex variable in Laplace domain."""))
temp.append(element.to_expr())
expr_mat_arg.append(temp)
if isinstance(arg, (tuple, list, Tuple)):
# Making nested Tuple (sympy.core.containers.Tuple) from nested list or nested Python tuple
arg = Tuple(*(Tuple(*r, sympify=False) for r in arg), sympify=False)
obj = super(TransferFunctionMatrix, cls).__new__(cls, arg)
obj._expr_mat = ImmutableMatrix(expr_mat_arg)
return obj
@classmethod
def from_Matrix(cls, matrix, var):
"""
Creates a new ``TransferFunctionMatrix`` efficiently from a SymPy Matrix of ``Expr`` objects.
Parameters
==========
matrix : ``ImmutableMatrix`` having ``Expr``/``Number`` elements.
var : Symbol
Complex variable of the Laplace transform which will be used by the
all the ``TransferFunction`` objects in the ``TransferFunctionMatrix``.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunctionMatrix
>>> from sympy import Matrix, pprint
>>> M = Matrix([[s, 1/s], [1/(s+1), s]])
>>> M_tf = TransferFunctionMatrix.from_Matrix(M, s)
>>> pprint(M_tf, use_unicode=False)
[ s 1]
[ - -]
[ 1 s]
[ ]
[ 1 s]
[----- -]
[s + 1 1]{t}
>>> M_tf.elem_poles()
[[[], [0]], [[-1], []]]
>>> M_tf.elem_zeros()
[[[0], []], [[], [0]]]
"""
return _to_TFM(matrix, var)
@property
def var(self):
"""
Returns the complex variable used by all the transfer functions or
``Series``/``Parallel`` objects in a transfer function matrix.
Examples
========
>>> from sympy.abc import p, s
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, Series, Parallel
>>> G1 = TransferFunction(p**2 + 2*p + 4, p - 6, p)
>>> G2 = TransferFunction(p, 4 - p, p)
>>> G3 = TransferFunction(0, p**4 - 1, p)
>>> G4 = TransferFunction(s + 1, s**2 + s + 1, s)
>>> S1 = Series(G1, G2)
>>> S2 = Series(-G3, Parallel(G2, -G1))
>>> tfm1 = TransferFunctionMatrix([[G1], [G2], [G3]])
>>> tfm1.var
p
>>> tfm2 = TransferFunctionMatrix([[-S1, -S2], [S1, S2]])
>>> tfm2.var
p
>>> tfm3 = TransferFunctionMatrix([[G4]])
>>> tfm3.var
s
"""
return self.args[0][0][0].var
@property
def num_inputs(self):
"""
Returns the number of inputs of the system.
Examples
========
>>> from sympy.abc import s, p
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
>>> G1 = TransferFunction(s + 3, s**2 - 3, s)
>>> G2 = TransferFunction(4, s**2, s)
>>> G3 = TransferFunction(p**2 + s**2, p - 3, s)
>>> tfm_1 = TransferFunctionMatrix([[G2, -G1, G3], [-G2, -G1, -G3]])
>>> tfm_1.num_inputs
3
See Also
========
num_outputs
"""
return self._expr_mat.shape[1]
@property
def num_outputs(self):
"""
Returns the number of outputs of the system.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunctionMatrix
>>> from sympy import Matrix
>>> M_1 = Matrix([[s], [1/s]])
>>> TFM = TransferFunctionMatrix.from_Matrix(M_1, s)
>>> print(TFM)
TransferFunctionMatrix(((TransferFunction(s, 1, s),), (TransferFunction(1, s, s),)))
>>> TFM.num_outputs
2
See Also
========
num_inputs
"""
return self._expr_mat.shape[0]
@property
def shape(self):
"""
Returns the shape of the transfer function matrix, that is, ``(# of outputs, # of inputs)``.
Examples
========
>>> from sympy.abc import s, p
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
>>> tf1 = TransferFunction(p**2 - 1, s**4 + s**3 - p, p)
>>> tf2 = TransferFunction(1 - p, p**2 - 3*p + 7, p)
>>> tf3 = TransferFunction(3, 4, p)
>>> tfm1 = TransferFunctionMatrix([[tf1, -tf2]])
>>> tfm1.shape
(1, 2)
>>> tfm2 = TransferFunctionMatrix([[-tf2, tf3], [tf1, -tf1]])
>>> tfm2.shape
(2, 2)
"""
return self._expr_mat.shape
def __neg__(self):
neg = -self._expr_mat
return _to_TFM(neg, self.var)
@_check_other_MIMO
def __add__(self, other):
if not isinstance(other, MIMOParallel):
return MIMOParallel(self, other)
other_arg_list = list(other.args)
return MIMOParallel(self, *other_arg_list)
@_check_other_MIMO
def __sub__(self, other):
return self + (-other)
@_check_other_MIMO
def __mul__(self, other):
if not isinstance(other, MIMOSeries):
return MIMOSeries(other, self)
other_arg_list = list(other.args)
return MIMOSeries(*other_arg_list, self)
def __getitem__(self, key):
trunc = self._expr_mat.__getitem__(key)
if isinstance(trunc, ImmutableMatrix):
return _to_TFM(trunc, self.var)
return TransferFunction.from_rational_expression(trunc, self.var)
def transpose(self):
"""Returns the transpose of the ``TransferFunctionMatrix`` (switched input and output layers)."""
transposed_mat = self._expr_mat.transpose()
return _to_TFM(transposed_mat, self.var)
def elem_poles(self):
"""
Returns the poles of each element of the ``TransferFunctionMatrix``.
.. note::
Actual poles of a MIMO system are NOT the poles of individual elements.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
>>> tf_1 = TransferFunction(3, (s + 1), s)
>>> tf_2 = TransferFunction(s + 6, (s + 1)*(s + 2), s)
>>> tf_3 = TransferFunction(s + 3, s**2 + 3*s + 2, s)
>>> tf_4 = TransferFunction(s + 2, s**2 + 5*s - 10, s)
>>> tfm_1 = TransferFunctionMatrix([[tf_1, tf_2], [tf_3, tf_4]])
>>> tfm_1
TransferFunctionMatrix(((TransferFunction(3, s + 1, s), TransferFunction(s + 6, (s + 1)*(s + 2), s)), (TransferFunction(s + 3, s**2 + 3*s + 2, s), TransferFunction(s + 2, s**2 + 5*s - 10, s))))
>>> tfm_1.elem_poles()
[[[-1], [-2, -1]], [[-2, -1], [-5/2 + sqrt(65)/2, -sqrt(65)/2 - 5/2]]]
See Also
========
elem_zeros
"""
return [[element.poles() for element in row] for row in self.doit().args[0]]
def elem_zeros(self):
"""
Returns the zeros of each element of the ``TransferFunctionMatrix``.
.. note::
Actual zeros of a MIMO system are NOT the zeros of individual elements.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
>>> tf_1 = TransferFunction(3, (s + 1), s)
>>> tf_2 = TransferFunction(s + 6, (s + 1)*(s + 2), s)
>>> tf_3 = TransferFunction(s + 3, s**2 + 3*s + 2, s)
>>> tf_4 = TransferFunction(s**2 - 9*s + 20, s**2 + 5*s - 10, s)
>>> tfm_1 = TransferFunctionMatrix([[tf_1, tf_2], [tf_3, tf_4]])
>>> tfm_1
TransferFunctionMatrix(((TransferFunction(3, s + 1, s), TransferFunction(s + 6, (s + 1)*(s + 2), s)), (TransferFunction(s + 3, s**2 + 3*s + 2, s), TransferFunction(s**2 - 9*s + 20, s**2 + 5*s - 10, s))))
>>> tfm_1.elem_zeros()
[[[], [-6]], [[-3], [4, 5]]]
See Also
========
elem_poles
"""
return [[element.zeros() for element in row] for row in self.doit().args[0]]
def eval_frequency(self, other):
"""
Evaluates system response of each transfer function in the ``TransferFunctionMatrix`` at any point in the real or complex plane.
Examples
========
>>> from sympy.abc import s
>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
>>> from sympy import I
>>> tf_1 = TransferFunction(3, (s + 1), s)
>>> tf_2 = TransferFunction(s + 6, (s + 1)*(s + 2), s)
>>> tf_3 = TransferFunction(s + 3, s**2 + 3*s + 2, s)
>>> tf_4 = TransferFunction(s**2 - 9*s + 20, s**2 + 5*s - 10, s)
>>> tfm_1 = TransferFunctionMatrix([[tf_1, tf_2], [tf_3, tf_4]])
>>> tfm_1
TransferFunctionMatrix(((TransferFunction(3, s + 1, s), TransferFunction(s + 6, (s + 1)*(s + 2), s)), (TransferFunction(s + 3, s**2 + 3*s + 2, s), TransferFunction(s**2 - 9*s + 20, s**2 + 5*s - 10, s))))
>>> tfm_1.eval_frequency(2)
Matrix([
[ 1, 2/3],
[5/12, 3/2]])
>>> tfm_1.eval_frequency(I*2)
Matrix([
[ 3/5 - 6*I/5, -I],
[3/20 - 11*I/20, -101/74 + 23*I/74]])
"""
mat = self._expr_mat.subs(self.var, other)
return mat.expand()
def _flat(self):
"""Returns flattened list of args in TransferFunctionMatrix"""
return [elem for tup in self.args[0] for elem in tup]
def _eval_evalf(self, prec):
"""Calls evalf() on each transfer function in the transfer function matrix"""
dps = prec_to_dps(prec)
mat = self._expr_mat.applyfunc(lambda a: a.evalf(n=dps))
return _to_TFM(mat, self.var)
def _eval_simplify(self, **kwargs):
"""Simplifies the transfer function matrix"""
simp_mat = self._expr_mat.applyfunc(lambda a: cancel(a, expand=False))
return _to_TFM(simp_mat, self.var)
def expand(self, **hints):
"""Expands the transfer function matrix"""
expand_mat = self._expr_mat.expand(**hints)
return _to_TFM(expand_mat, self.var)
class StateSpace(LinearTimeInvariant):
r"""
State space model (ssm) of a linear, time invariant control system.
Represents the standard state-space model with A, B, C, D as state-space matrices.
This makes the linear control system:
(1) x'(t) = A * x(t) + B * u(t); x in R^n , u in R^k
(2) y(t) = C * x(t) + D * u(t); y in R^m
where u(t) is any input signal, y(t) the corresponding output, and x(t) the system's state.
Parameters
==========
A : Matrix
The State matrix of the state space model.
B : Matrix
The Input-to-State matrix of the state space model.
C : Matrix
The State-to-Output matrix of the state space model.
D : Matrix
The Feedthrough matrix of the state space model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
The easiest way to create a StateSpaceModel is via four matrices:
>>> A = Matrix([[1, 2], [1, 0]])
>>> B = Matrix([1, 1])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([0])
>>> StateSpace(A, B, C, D)
StateSpace(Matrix([
[1, 2],
[1, 0]]), Matrix([
[1],
[1]]), Matrix([[0, 1]]), Matrix([[0]]))
One can use less matrices. The rest will be filled with a minimum of zeros:
>>> StateSpace(A, B)
StateSpace(Matrix([
[1, 2],
[1, 0]]), Matrix([
[1],
[1]]), Matrix([[0, 0]]), Matrix([[0]]))
See Also
========
TransferFunction, TransferFunctionMatrix
References
==========
.. [1] https://en.wikipedia.org/wiki/State-space_representation
.. [2] https://in.mathworks.com/help/control/ref/ss.html
"""
def __new__(cls, A=None, B=None, C=None, D=None):
if A is None:
A = zeros(1)
if B is None:
B = zeros(A.rows, 1)
if C is None:
C = zeros(1, A.cols)
if D is None:
D = zeros(C.rows, B.cols)
A = _sympify(A)
B = _sympify(B)
C = _sympify(C)
D = _sympify(D)
if (isinstance(A, ImmutableDenseMatrix) and isinstance(B, ImmutableDenseMatrix) and
isinstance(C, ImmutableDenseMatrix) and isinstance(D, ImmutableDenseMatrix)):
# Check State Matrix is square
if A.rows != A.cols:
raise ShapeError("Matrix A must be a square matrix.")
# Check State and Input matrices have same rows
if A.rows != B.rows:
raise ShapeError("Matrices A and B must have the same number of rows.")
# Check Ouput and Feedthrough matrices have same rows
if C.rows != D.rows:
raise ShapeError("Matrices C and D must have the same number of rows.")
# Check State and Ouput matrices have same columns
if A.cols != C.cols:
raise ShapeError("Matrices A and C must have the same number of columns.")
# Check Input and Feedthrough matrices have same columns
if B.cols != D.cols:
raise ShapeError("Matrices B and D must have the same number of columns.")
obj = super(StateSpace, cls).__new__(cls, A, B, C, D)
obj._A = A
obj._B = B
obj._C = C
obj._D = D
# Determine if the system is SISO or MIMO
num_outputs = D.rows
num_inputs = D.cols
if num_inputs == 1 and num_outputs == 1:
obj._is_SISO = True
obj._clstype = SISOLinearTimeInvariant
else:
obj._is_SISO = False
obj._clstype = MIMOLinearTimeInvariant
return obj
else:
raise TypeError("A, B, C and D inputs must all be sympy Matrices.")
@property
def state_matrix(self):
"""
Returns the state matrix of the model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[1, 2], [1, 0]])
>>> B = Matrix([1, 1])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> ss.state_matrix
Matrix([
[1, 2],
[1, 0]])
"""
return self._A
@property
def input_matrix(self):
"""
Returns the input matrix of the model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[1, 2], [1, 0]])
>>> B = Matrix([1, 1])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> ss.input_matrix
Matrix([
[1],
[1]])
"""
return self._B
@property
def output_matrix(self):
"""
Returns the output matrix of the model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[1, 2], [1, 0]])
>>> B = Matrix([1, 1])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> ss.output_matrix
Matrix([[0, 1]])
"""
return self._C
@property
def feedforward_matrix(self):
"""
Returns the feedforward matrix of the model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[1, 2], [1, 0]])
>>> B = Matrix([1, 1])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> ss.feedforward_matrix
Matrix([[0]])
"""
return self._D
@property
def num_states(self):
"""
Returns the number of states of the model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[1, 2], [1, 0]])
>>> B = Matrix([1, 1])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> ss.num_states
2
"""
return self._A.rows
@property
def num_inputs(self):
"""
Returns the number of inputs of the model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[1, 2], [1, 0]])
>>> B = Matrix([1, 1])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> ss.num_inputs
1
"""
return self._D.cols
@property
def num_outputs(self):
"""
Returns the number of outputs of the model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[1, 2], [1, 0]])
>>> B = Matrix([1, 1])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> ss.num_outputs
1
"""
return self._D.rows
def _eval_evalf(self, prec):
"""
Returns state space model where numerical expressions are evaluated into floating point numbers.
"""
dps = prec_to_dps(prec)
return StateSpace(
self._A.evalf(n = dps),
self._B.evalf(n = dps),
self._C.evalf(n = dps),
self._D.evalf(n = dps))
def _eval_rewrite_as_TransferFunction(self, *args):
"""
Returns the equivalent Transfer Function of the state space model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import TransferFunction, StateSpace
>>> A = Matrix([[-5, -1], [3, -1]])
>>> B = Matrix([2, 5])
>>> C = Matrix([[1, 2]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> ss.rewrite(TransferFunction)
[[TransferFunction(12*s + 59, s**2 + 6*s + 8, s)]]
"""
s = Symbol('s')
n = self._A.shape[0]
I = eye(n)
G = self._C*(s*I - self._A).solve(self._B) + self._D
G = G.simplify()
to_tf = lambda expr: TransferFunction.from_rational_expression(expr, s)
tf_mat = [[to_tf(expr) for expr in sublist] for sublist in G.tolist()]
return tf_mat
def __add__(self, other):
"""
Add two State Space systems (parallel connection).
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A1 = Matrix([[1]])
>>> B1 = Matrix([[2]])
>>> C1 = Matrix([[-1]])
>>> D1 = Matrix([[-2]])
>>> A2 = Matrix([[-1]])
>>> B2 = Matrix([[-2]])
>>> C2 = Matrix([[1]])
>>> D2 = Matrix([[2]])
>>> ss1 = StateSpace(A1, B1, C1, D1)
>>> ss2 = StateSpace(A2, B2, C2, D2)
>>> ss1 + ss2
StateSpace(Matrix([
[1, 0],
[0, -1]]), Matrix([
[ 2],
[-2]]), Matrix([[-1, 1]]), Matrix([[0]]))
"""
# Check for scalars
if isinstance(other, (int, float, complex, Symbol)):
A = self._A
B = self._B
C = self._C
D = self._D.applyfunc(lambda element: element + other)
else:
# Check nature of system
if not isinstance(other, StateSpace):
raise ValueError("Addition is only supported for 2 State Space models.")
# Check dimensions of system
elif ((self.num_inputs != other.num_inputs) or (self.num_outputs != other.num_outputs)):
raise ShapeError("Systems with incompatible inputs and outputs cannot be added.")
m1 = (self._A).row_join(zeros(self._A.shape[0], other._A.shape[-1]))
m2 = zeros(other._A.shape[0], self._A.shape[-1]).row_join(other._A)
A = m1.col_join(m2)
B = self._B.col_join(other._B)
C = self._C.row_join(other._C)
D = self._D + other._D
return StateSpace(A, B, C, D)
def __radd__(self, other):
"""
Right add two State Space systems.
Examples
========
>>> from sympy.physics.control import StateSpace
>>> s = StateSpace()
>>> 5 + s
StateSpace(Matrix([[0]]), Matrix([[0]]), Matrix([[0]]), Matrix([[5]]))
"""
return self + other
def __sub__(self, other):
"""
Subtract two State Space systems.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A1 = Matrix([[1]])
>>> B1 = Matrix([[2]])
>>> C1 = Matrix([[-1]])
>>> D1 = Matrix([[-2]])
>>> A2 = Matrix([[-1]])
>>> B2 = Matrix([[-2]])
>>> C2 = Matrix([[1]])
>>> D2 = Matrix([[2]])
>>> ss1 = StateSpace(A1, B1, C1, D1)
>>> ss2 = StateSpace(A2, B2, C2, D2)
>>> ss1 - ss2
StateSpace(Matrix([
[1, 0],
[0, -1]]), Matrix([
[ 2],
[-2]]), Matrix([[-1, -1]]), Matrix([[-4]]))
"""
return self + (-other)
def __rsub__(self, other):
"""
Right subtract two tate Space systems.
Examples
========
>>> from sympy.physics.control import StateSpace
>>> s = StateSpace()
>>> 5 - s
StateSpace(Matrix([[0]]), Matrix([[0]]), Matrix([[0]]), Matrix([[5]]))
"""
return other + (-self)
def __neg__(self):
"""
Returns the negation of the state space model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[-5, -1], [3, -1]])
>>> B = Matrix([2, 5])
>>> C = Matrix([[1, 2]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> -ss
StateSpace(Matrix([
[-5, -1],
[ 3, -1]]), Matrix([
[2],
[5]]), Matrix([[-1, -2]]), Matrix([[0]]))
"""
return StateSpace(self._A, self._B, -self._C, -self._D)
def __mul__(self, other):
"""
Multiplication of two State Space systems (serial connection).
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[-5, -1], [3, -1]])
>>> B = Matrix([2, 5])
>>> C = Matrix([[1, 2]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> ss*5
StateSpace(Matrix([
[-5, -1],
[ 3, -1]]), Matrix([
[2],
[5]]), Matrix([[5, 10]]), Matrix([[0]]))
"""
# Check for scalars
if isinstance(other, (int, float, complex, Symbol)):
A = self._A
B = self._B
C = self._C.applyfunc(lambda element: element*other)
D = self._D.applyfunc(lambda element: element*other)
else:
# Check nature of system
if not isinstance(other, StateSpace):
raise ValueError("Multiplication is only supported for 2 State Space models.")
# Check dimensions of system
elif self.num_inputs != other.num_outputs:
raise ShapeError("Systems with incompatible inputs and outputs cannot be multiplied.")
m1 = (other._A).row_join(zeros(other._A.shape[0], self._A.shape[1]))
m2 = (self._B * other._C).row_join(self._A)
A = m1.col_join(m2)
B = (other._B).col_join(self._B * other._D)
C = (self._D * other._C).row_join(self._C)
D = self._D * other._D
return StateSpace(A, B, C, D)
def __rmul__(self, other):
"""
Right multiply two tate Space systems.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[-5, -1], [3, -1]])
>>> B = Matrix([2, 5])
>>> C = Matrix([[1, 2]])
>>> D = Matrix([0])
>>> ss = StateSpace(A, B, C, D)
>>> 5*ss
StateSpace(Matrix([
[-5, -1],
[ 3, -1]]), Matrix([
[10],
[25]]), Matrix([[1, 2]]), Matrix([[0]]))
"""
if isinstance(other, (int, float, complex, Symbol)):
A = self._A
C = self._C
B = self._B.applyfunc(lambda element: element*other)
D = self._D.applyfunc(lambda element: element*other)
return StateSpace(A, B, C, D)
else:
return self*other
def __repr__(self):
A_str = self._A.__repr__()
B_str = self._B.__repr__()
C_str = self._C.__repr__()
D_str = self._D.__repr__()
return f"StateSpace(\n{A_str},\n\n{B_str},\n\n{C_str},\n\n{D_str})"
def append(self, other):
"""
Returns the first model appended with the second model. The order is preserved.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A1 = Matrix([[1]])
>>> B1 = Matrix([[2]])
>>> C1 = Matrix([[-1]])
>>> D1 = Matrix([[-2]])
>>> A2 = Matrix([[-1]])
>>> B2 = Matrix([[-2]])
>>> C2 = Matrix([[1]])
>>> D2 = Matrix([[2]])
>>> ss1 = StateSpace(A1, B1, C1, D1)
>>> ss2 = StateSpace(A2, B2, C2, D2)
>>> ss1.append(ss2)
StateSpace(Matrix([
[1, 0],
[0, -1]]), Matrix([
[2, 0],
[0, -2]]), Matrix([
[-1, 0],
[ 0, 1]]), Matrix([
[-2, 0],
[ 0, 2]]))
"""
n = self.num_states + other.num_states
m = self.num_inputs + other.num_inputs
p = self.num_outputs + other.num_outputs
A = zeros(n, n)
B = zeros(n, m)
C = zeros(p, n)
D = zeros(p, m)
A[:self.num_states, :self.num_states] = self._A
A[self.num_states:, self.num_states:] = other._A
B[:self.num_states, :self.num_inputs] = self._B
B[self.num_states:, self.num_inputs:] = other._B
C[:self.num_outputs, :self.num_states] = self._C
C[self.num_outputs:, self.num_states:] = other._C
D[:self.num_outputs, :self.num_inputs] = self._D
D[self.num_outputs:, self.num_inputs:] = other._D
return StateSpace(A, B, C, D)
def observability_matrix(self):
"""
Returns the observability matrix of the state space model:
[C, C * A^1, C * A^2, .. , C * A^(n-1)]; A in R^(n x n), C in R^(m x k)
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[-1.5, -2], [1, 0]])
>>> B = Matrix([0.5, 0])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([1])
>>> ss = StateSpace(A, B, C, D)
>>> ob = ss.observability_matrix()
>>> ob
Matrix([
[0, 1],
[1, 0]])
References
==========
.. [1] https://in.mathworks.com/help/control/ref/statespacemodel.obsv.html
"""
n = self.num_states
ob = self._C
for i in range(1,n):
ob = ob.col_join(self._C * self._A**i)
return ob
def observable_subspace(self):
"""
Returns the observable subspace of the state space model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[-1.5, -2], [1, 0]])
>>> B = Matrix([0.5, 0])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([1])
>>> ss = StateSpace(A, B, C, D)
>>> ob_subspace = ss.observable_subspace()
>>> ob_subspace
[Matrix([
[0],
[1]]), Matrix([
[1],
[0]])]
"""
return self.observability_matrix().columnspace()
def is_observable(self):
"""
Returns if the state space model is observable.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[-1.5, -2], [1, 0]])
>>> B = Matrix([0.5, 0])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([1])
>>> ss = StateSpace(A, B, C, D)
>>> ss.is_observable()
True
"""
return self.observability_matrix().rank() == self.num_states
def controllability_matrix(self):
"""
Returns the controllability matrix of the system:
[B, A * B, A^2 * B, .. , A^(n-1) * B]; A in R^(n x n), B in R^(n x m)
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[-1.5, -2], [1, 0]])
>>> B = Matrix([0.5, 0])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([1])
>>> ss = StateSpace(A, B, C, D)
>>> ss.controllability_matrix()
Matrix([
[0.5, -0.75],
[ 0, 0.5]])
References
==========
.. [1] https://in.mathworks.com/help/control/ref/statespacemodel.ctrb.html
"""
co = self._B
n = self._A.shape[0]
for i in range(1, n):
co = co.row_join(((self._A)**i) * self._B)
return co
def controllable_subspace(self):
"""
Returns the controllable subspace of the state space model.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[-1.5, -2], [1, 0]])
>>> B = Matrix([0.5, 0])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([1])
>>> ss = StateSpace(A, B, C, D)
>>> co_subspace = ss.controllable_subspace()
>>> co_subspace
[Matrix([
[0.5],
[ 0]]), Matrix([
[-0.75],
[ 0.5]])]
"""
return self.controllability_matrix().columnspace()
def is_controllable(self):
"""
Returns if the state space model is controllable.
Examples
========
>>> from sympy import Matrix
>>> from sympy.physics.control import StateSpace
>>> A = Matrix([[-1.5, -2], [1, 0]])
>>> B = Matrix([0.5, 0])
>>> C = Matrix([[0, 1]])
>>> D = Matrix([1])
>>> ss = StateSpace(A, B, C, D)
>>> ss.is_controllable()
True
"""
return self.controllability_matrix().rank() == self.num_states
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