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GeezerVison is showing github.com — DOC: Clarify process-noise input dimensions in LQE by huxiaoyi-ovo · Pull Request #1256 · python-control/python-control

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@huxiaoyi-ovo

@huxiaoyi-ovo huxiaoyi-ovo commented Oct 13, 2026

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Summary

Clarify the default process-noise input matrix and covariance dimensions for the lqe and dlqe estimators.

Addresses #1159 without changing the existing estimator behavior.

Changes

  • Document that lqe(sys, QN, RN) uses sys.B as the process-noise input matrix G.
  • Clarify that QN must have shape (sys.ninputs, sys.ninputs), which is not necessarily the same as the state dimension.
  • Explain how to specify a different noise input matrix using the explicit matrix-based interface.
  • Apply the same clarification to the discrete-time dlqe interface.
  • Correct the documentation to identify StateSpace as the supported system input and replace incorrect dlqr references with dlqe.

Changes are limited to control/stochsys.py and doc/stochastic.rst.

No estimator algorithms, public APIs, or runtime behavior are modified.

Testing

All five CI workflows passed on the fork:

  • Ruff
  • Doctest
  • Conda-based pytest
  • Slycot from source
  • Setup, Examples, and Notebooks

This is a documentation-only change. Existing tests passed without modification.

AI assistance was used for documentation drafting, source review, and validation.

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huxiaoyi-ovo added 5 commits October 12, 2026 20:02
@huxiaoyi-ovo
docs(stochsys): state LQE noise-input dimensions explicitly
11b9782
@huxiaoyi-ovo
docs(stochastic): explain G=B and QN input-channel covariance
c369c46
@huxiaoyi-ovo
docs(stochastic): separate estimator guidance paragraphs
283f442
@huxiaoyi-ovo
docs(stochsys): specify supported state-space LQE input
e3636cf
@huxiaoyi-ovo
docs(stochastic): correct discrete-time estimator references
a91905a
@coveralls

coveralls commented Oct 13, 2026

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Coverage Status

coverage: 94.757%. remained the same — huxiaoyi-ovo:docs/lqe-noise-input-dimensions-1159 into python-control:main

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2 participants

@huxiaoyi-ovo @coveralls
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