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Merged
murrayrm merged 13 commits into from
Aug 6, 2024
Merged

Improve markov function, add mimo support, change api to TimeResponseData #1022

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f2f0e3e
Improve markov function, add mimo support, change api to TimeResponse…
KybernetikJo Jul 3, 2024
6c95fbc
Fix inputs dimension
KybernetikJo Jul 4, 2024
14ce769
Add plot_inputs=False to TimeResponseData output
KybernetikJo Jul 4, 2024
efa3f39
Add markov example
KybernetikJo Jul 4, 2024
a597bbc
Add markov example, add example to doc
KybernetikJo Jul 4, 2024
6023480
Change output to ndarray
KybernetikJo Jul 7, 2024
92307f0
Update example, values taken from a book
KybernetikJo Jul 7, 2024
a646100
Change API to work with ndarray and TimeResponseData as input
KybernetikJo Jul 7, 2024
42962f2
Update pytest
KybernetikJo Jul 7, 2024
a3276b7
Refactor api, keep old api working
KybernetikJo Jul 11, 2024
7ac6ee0
Remove parenthesis form if, update docstrings and comments
KybernetikJo Jul 15, 2024
8d62775
Change default value of dt to None, add equally spaced check for resp…
KybernetikJo Jul 15, 2024
e61ac53
Add unit tests to increase code coverage
KybernetikJo Jul 15, 2024
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Refactor api, keep old api working
  • Loading branch information
@KybernetikJo
KybernetikJo committed Jul 11, 2024
commit a3276b72f32a0b2174f4724252e8877be1465b17
58 changes: 24 additions & 34 deletions control/modelsimp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -402,7 +402,7 @@ def era(YY, m, n, nin, nout, r):
raise NotImplementedError('This function is not implemented yet.')


def markov(*args, **kwargs):
def markov(*args, m=None, transpose=False, dt=True, truncate=False):
"""markov(Y, U, [, m])

Calculate the first `m` Markov parameters [D CB CAB ...]
Expand All @@ -420,12 +420,12 @@ def markov(*args, **kwargs):
the input data is less than the desired number of Markov parameters (a
warning message is generated in this case).

The function can be called with either 1, 2, or 3 arguments:
The function can be called with either 1, 2 or 3 arguments:

* ``K, S, E = lqr(response)``
* ``K, S, E = lqr(respnose, m)``
* ``K, S, E = lqr(Y, U)``
* ``K, S, E = lqr(Y, U, m)``
* ``H = markov(response)``
* ``H = markov(respnose, m)``
* ``H = markov(Y, U)``
* ``H = markov(Y, U, m)``

where `response` is an `TimeResponseData` object, and `Y`, `U`, are 1D or 2D
array and m is an integer.
Expand All @@ -446,26 +446,20 @@ def markov(*args, **kwargs):
Number of Markov parameters to output. Defaults to len(U).
dt : True of float, optional
True indicates discrete time with unspecified sampling time,
positive number is discrete time with specified sampling time.
It can be used to scale the markov parameters in order to match
the impulse response of this library.
Default values is True.
positive number is discrete time with specified sampling time.It
can be used to scale the markov parameters in order to match the

@murrayrm murrayrm Jul 13, 2024

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Markov should be capitalized.

@KybernetikJo KybernetikJo Jul 16, 2024

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Done.

impulse response of this library. Default is True.
truncate : bool, optional
Do not use first m equation for least least squares.
Default value is False.
Do not use first m equation for least least squares. Default is False.
transpose : bool, optional
Assume that input data is transposed relative to the standard
:ref:`time-series-convention`. For TimeResponseData this parameter
is ignored. Default is False.

Returns
-------
H : ndarray
First m Markov parameters, [D CB CAB ...]


Notes
-----
It works for SISO and MIMO systems.

This function does comply with the Python Control Library
:ref:`time-series-convention` for representation of time series data.

References
----------
Expand Down Expand Up @@ -494,25 +488,21 @@ def markov(*args, **kwargs):
transpose = args[0].transpose
if args[0].transpose and not args[0].issiso:
Umat, Ymat = np.transpose(Umat), np.transpose(Ymat)
index = 1
if (len(args) == 2):
m = args[1]
elif (len(args) > 2):
raise ControlArgument("too many positional arguments")
else:
if (len(args) < 2):

@murrayrm murrayrm Jul 13, 2024

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OK to remove parens (here and below).

@KybernetikJo KybernetikJo Jul 16, 2024

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Done.

raise ControlArgument("not enough input arguments")
Umat = np.array(args[0], ndmin=2)
Ymat = np.array(args[1], ndmin=2)
transpose = kwargs.pop('transpose', False)
Umat = np.array(args[1], ndmin=2)
Ymat = np.array(args[0], ndmin=2)
if transpose:
Umat, Ymat = np.transpose(Umat), np.transpose(Ymat)
index = 2


if (len(args) > index):
m = args[index]
else:
m = None

dt = kwargs.pop('dt', True)
truncate = kwargs.pop('truncate', False)
if (len(args) == 3):
m = args[2]
elif (len(args) > 3):
raise ControlArgument("too many positional arguments")

# Make sure the number of time points match
if Umat.shape[1] != Ymat.shape[1]:
Expand Down
25 changes: 15 additions & 10 deletions control/tests/modelsimp_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,11 +45,11 @@ def testMarkovSignature(self):
m = 3
Htrue = np.array([1., 0., 0.])

H = markov(Y, U, m, transpose=False)
H = markov(Y, U, m=m, transpose=False)
np.testing.assert_array_almost_equal(H, Htrue)

response.transpose=False
H = markov(response, m)
H = markov(response, m=m)
np.testing.assert_array_almost_equal(H, Htrue)

# Make sure that transposed data also works
Expand All @@ -68,20 +68,25 @@ def testMarkovSignature(self):
H = markov(response, m)
np.testing.assert_array_almost_equal(H, Htrue)

H = markov(Y, U, m=m)
np.testing.assert_array_almost_equal(H, Htrue)

H = markov(response, m=m)
np.testing.assert_array_almost_equal(H, Htrue)

# Test example from docstring
# TODO: There is a problem here
# Htrue = np.array([1., 0.5, 0.])
# TODO: There is a problem here, last markov parameter does not fit
# the approximation error could be to big
Htrue = np.array([0, 1., -0.5])
T = np.linspace(0, 10, 100)
U = np.ones((1, 100))
T, Y = forced_response(tf([1], [1, 0.5], True), T, U)
H = markov(Y, U, 3, transpose=False)
#np.testing.assert_array_almost_equal(H, Htrue)
H = markov(Y, U, 4, transpose=False)
np.testing.assert_array_almost_equal(H[:3], Htrue[:3])

T = np.linspace(0, 10, 100)
U = np.ones((1, 100))
response = forced_response(tf([1], [1, 0.5], True), T, U)
H = markov(response, 3)
#np.testing.assert_array_almost_equal(H, Htrue)
H = markov(response, 4)
np.testing.assert_array_almost_equal(H[:3], Htrue[:3])

# Test example from issue #395
inp = np.array([1, 2])
Expand Down