Chance Constrained Covariance Control for Linear Stochastic Systems With\n Output Feedback

2020/01/13 by Jack Ridderhof, Ridderhof, Jack, Kazuhide Okamoto +3 · 2 citations
Engineering · #Advanced Control Systems Optimization #FOS: Mathematics #Fault Detection and Control Systems #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.2001.04544

openalex publication_date 2020/01/13 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28

Abstract

We consider the problem of steering, via output feedback, the state\ndistribution of a discrete-time, linear stochastic system from an initial\nGaussian distribution to a terminal Gaussian distribution with prescribed mean\nand maximum covariance, subject to probabilistic path constraints on the state.\nThe filtered state is obtained via a Kalman filter, and the problem is\nformulated as a deterministic convex program in terms of the distribution of\nthe filtered state. We observe that, in the presence of constraints on the\nstate covariance, and in contrast to classical Linear Quadratic Gaussian (LQG)\ncontrol, the optimal feedback control depends on both the process noise and the\nobservation model. The effectiveness of the proposed approach is verified using\na numerical example.\n

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