Chance Constrained Covariance Control for Linear Stochastic Systems With Output Feedback

We consider the problem of steering, via out-put feedback, the state distribution of a discrete-time, linear stochastic system from an initial Gaussian distribution to a terminal Gaussian distribution with prescribed mean and max-imum covariance, subject to probabilistic path constraints on the stat...

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Bibliographic Details
Published inProceedings of the IEEE Conference on Decision & Control pp. 1758 - 1763
Main Authors Ridderhof, Jack, Okamoto, Kazuhide, Tsiotras, Panagiotis
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.12.2020
Subjects
Estimation error
Kalman filters
Noise measurement
Stochastic processes
Stochastic systems
Vehicle dynamics
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ISSN2576-2370
DOI10.1109/CDC42340.2020.9303731

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Summary:We consider the problem of steering, via out-put feedback, the state distribution of a discrete-time, linear stochastic system from an initial Gaussian distribution to a terminal Gaussian distribution with prescribed mean and max-imum covariance, subject to probabilistic path constraints on the state. The filtered state is obtained via a Kalman filter, and the problem is formulated as a deterministic convex program in terms of the distribution of the filtered state. We observe that, in the presence of constraints on the state covariance, and in contrast to classical Linear Quadratic Gaussian (LQG) control, the optimal feedback control depends on both the process noise and the observation model. The effectiveness of the proposed approach is verified using a numerical example.
ISSN:2576-2370
DOI:10.1109/CDC42340.2020.9303731