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Implement okid#1031

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KybernetikJo wants to merge2 commits intopython-control:main
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@KybernetikJoKybernetikJo commentedJul 15, 2024•
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This PR implements okid=observer_kalman_identification.

The api should be the same or very similar to that ofct.markov.
An additional goal is forct.okid andct.era to work well together.

T=np.linspace(0,10,100)U=np.ones((1,100))T,Y=ct.forced_response(ct.tf([1], [1,0.5],True),T,U)H=ct.okid(Y,U,3)
T=np.linspace(0,10,100)U=np.ones((1,100))response=ct.forced_response(ct.tf([1], [1,0.5],True),T,U)H=ct.okid(response,3)

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coveralls commentedJul 15, 2024•
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Coverage Status

coverage: 94.127% (-0.6%) from 94.752%
when pulling5abbfe1 on KybernetikJo:implement_okid
intof73e893 on python-control:main.

@KybernetikJoKybernetikJoforce-pushed theimplement_okid branch 2 times, most recently from860acb7 to67d6ef0CompareJuly 16, 2024 09:47
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@KybernetikJo This PR needs to be rebased on main (and okid should be expanded toobserver_kalman_identification (orobserver_kalman_id to keep things a bit shorter?).

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Overall this looks good. Some stylistic issues that it would be useful to update for consistency with standard Python style. Perhaps also say something here (or in themarkov command) about the relationship betweenokid andmarkov?

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

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Should we add a "See Also" section and referencemarkov? Perhaps also a note here (and inmarkov) about how this differs from themarkov command?

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Would that be enough?

See Also

markov

Notes

The :func:~control.markov command estimates the Markov parameters directly, which can be hard for slightly damped systems.
The :func:~control.observer_kalman_identification command uses a Kalman filter, which is better suited for slightly damped systems.

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@KybernetikJo I will be doing a release of python-control in the coming days. If you have time to update this PR prior to that, we can include in v0.10.1. Otherwise, it can go in the next release.

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KybernetikJo commentedOct 19, 2024•
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@murrayrm

@KybernetikJo I will be doing a release of python-control in the coming days. If you have time to update this PR prior to that, we can include in v0.10.1. Otherwise, it can go in the next release.

Sorry for the late reply, I had no time at all.

State of okid:

  • I think code is OK, docstring is OK, example is OK!

But:

  • I have to think about the m parameter again. In the paper, the algorithm estimates (m+1) parameters, but it should be consistent with makrov's m parameters.
  • I am not satisfied with my unit tests, maybe someone has suggestions for improvement.

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