GeezerVison

Addresses #1173.

This keeps the existing dlqe default behavior unchanged: by default it still returns the one-step predictor gain A P C^T (C P C^T + R_N)^-1.

The change adds return_filter_form=True for callers that need the filter-form correction gain P C^T (C P C^T + R_N)^-1, which cannot be recovered from the default predictor gain when A is singular. The returned covariance is documented as the steady-state prediction/prior covariance used by both gain formulas.

I also corrected the dlqe Riccati equation documentation and the stochastic docs wording that referred to dlqr/lqr in the estimator section.

Tests run locally:

• python -m pytest control/tests/stochsys_test.py::test_DLQE_return_filter_form control/tests/stochsys_test.py::test_lqe_discrete -q
• python -m pytest control/tests/stochsys_test.py -q
• python -m pytest control/tests/docstrings_test.py -q
• python -m pytest control/tests/mateqn_test.py::TestMatrixEquations::test_dare -q
• python -m pytest control/tests/kwargs_test.py::test_unrecognized_kwargs -q -k "dlqe or lqe"
• python -m ruff check control/stochsys.py control/tests/stochsys_test.py
• python -m compileall -q control\stochsys.py control\tests\stochsys_test.py
• python -m numpydoc render control.stochsys.dlqe

AI Disclosure: Codex (ChatGPT 5.5) was used during code navigation, initial drafting, and PR text preparation. All logic, code changes, and test cases have been manually reviewed, verified, and tested locally by the author in accordance with the NumPy AI Policy.