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Optimization-based and moving horizon estimation#877
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coveralls commentedMar 25, 2023 • edited
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| :math:`Y[k] \in \mathbb{R}^p`, and :math:`V[k] \in \mathbb{R}^q` and | ||
| :math:`W[k] \in \mathbb{R}^p` represent random processes that are not | ||
| necessarily Gaussian white noise processes. The estimation problem that we | ||
| wish to solve is to find the estimate :math:`\hat x[\cdot]` that matches |
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Should these be capital X-hat and Y-hat in keeping with the notation above?
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Never mind, I realize now they're random processes.
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This looks like a great feature, LGTM
Co-authored-by: Sawyer Fuller <58706249+sawyerbfuller@users.noreply.github.com>
This PR adds new functionality for computing the optimal estimate for a (nonlinear) I/O system using an explicit cost function of a fixed window of applied inputs and measured outputs. This optimal estimation problem is then used to create a system for solving moving horizon estimation (MHE) problems. Documentation and unit tests are included.
Summary of changes:
obc.OptimalEstimationProblemclass andobc.create_mhe_iosystem()function, which implement that main new functionality.gaussian_likelyhood_cost()to create cost function corresponding to Gaussian likelihoods for use in optimal estimation.disturbance_range_constraint()to create a range constraint on disturbances.iosys.pyand use that functionality consistently forobc.create_estimator_iosystemandobc.OptimalEstimationProblem.config.pyand updated legacy keyword processing class to use that functionality.@pytest.mark.slow), which can be used during development to skip slow tests by runningpytest -m "not slow".