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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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Change default value of dt to None, add equally spaced check for resp… 
…onse time data
  • Loading branch information
@KybernetikJo
KybernetikJo committed Jul 15, 2024
commit 8d62775303e18fabff1cbd5927e3beaf6f2a765f
22 changes: 16 additions & 6 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, m=None, transpose=False, dt=True, truncate=False):
def markov(*args, m=None, transpose=False, dt=None, truncate=False):
"""markov(Y, U, [, m])

Calculate the first `m` Markov parameters [D CB CAB ...] from data.
Expand Down Expand Up @@ -448,7 +448,9 @@ def markov(*args, m=None, transpose=False, dt=True, truncate=False):
True indicates discrete time with unspecified sampling time and a
positive float is discrete time with the specified sampling time.
It can be used to scale the Markov parameters in order to match
the unit-area impulse response of python-control. Default is True.
the unit-area impulse response of python-control. Default is True
for array_like and dt=data.time[1]-data.time[0] for
TimeResponseData as input.
truncate : bool, optional
Do not use first m equation for least squares. Default is False.
transpose : bool, optional
Expand Down Expand Up @@ -483,10 +485,16 @@ def markov(*args, m=None, transpose=False, dt=True, truncate=False):
raise ControlArgument("not enough input arguments")

if isinstance(args[0], TimeResponseData):
Umat = np.array(args[0].inputs, ndmin=2)
Ymat = np.array(args[0].outputs, ndmin=2)
transpose = args[0].transpose
if args[0].transpose and not args[0].issiso:
data = args[0]
Umat = np.array(data.inputs, ndmin=2)
Ymat = np.array(data.outputs, ndmin=2)
if dt is None:
dt = data.time[1] - data.time[0]
if not np.allclose(np.diff(data.time), dt):
raise ValueError("response time values must be equally "
"spaced.")
transpose = data.transpose
if data.transpose and not data.issiso:
Umat, Ymat = np.transpose(Umat), np.transpose(Ymat)
if len(args) == 2:
m = args[1]
Expand All @@ -497,6 +505,8 @@ def markov(*args, m=None, transpose=False, dt=True, truncate=False):
raise ControlArgument("not enough input arguments")
Umat = np.array(args[1], ndmin=2)
Ymat = np.array(args[0], ndmin=2)
if dt is None:
dt = True
if transpose:
Umat, Ymat = np.transpose(Umat), np.transpose(Ymat)
if len(args) == 3:
Expand Down
22 changes: 18 additions & 4 deletions control/tests/modelsimp_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,11 +81,11 @@ def testMarkovSignature(self):
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, 4, transpose=False)
H = markov(Y, U, 4, dt=True)
np.testing.assert_array_almost_equal(H[:3], Htrue[:3])

response = forced_response(tf([1], [1, 0.5], True), T, U)
H = markov(response, 4)
H = markov(response, 4, dt=True)
np.testing.assert_array_almost_equal(H[:3], Htrue[:3])

# Test example from issue #395
Expand Down Expand Up @@ -164,14 +164,28 @@ def testMarkovResults(self, k, m, n):
# Generate input/output data
T = np.array(range(n)) * Ts
U = np.cos(T) + np.sin(T/np.pi)
response = forced_response(Hd, T, U, squeeze=True)
Mcomp = markov(response, m)

ir_true = impulse_response(Hd,T)
Mtrue_scaled = ir_true[1][:m]

T, Y = forced_response(Hd, T, U, squeeze=True)
Mcomp = markov(Y, U, m, dt=True)
Mcomp_scaled = markov(Y, U, m, dt=Ts)

# Compare to results from markov()
# experimentally determined probability to get non matching results
# with rtot=1e-6 and atol=1e-8 due to numerical errors
# for k=5, m=n=10: 0.015 %
np.testing.assert_allclose(Mtrue, Mcomp, rtol=1e-6, atol=1e-8)
np.testing.assert_allclose(Mtrue_scaled, Mcomp_scaled, rtol=1e-6, atol=1e-8)

response = forced_response(Hd, T, U, squeeze=True)
Mcomp = markov(response, m, dt=True)
Mcomp_scaled = markov(response, m, dt=Ts)

np.testing.assert_allclose(Mtrue, Mcomp, rtol=1e-6, atol=1e-8)
np.testing.assert_allclose(Mtrue_scaled, Mcomp_scaled, rtol=1e-6, atol=1e-8)


def testModredMatchDC(self):
#balanced realization computed in matlab for the transfer function:
Expand Down