Computationally Efficient Chance Constrained Covariance Control with Output Feedback

Joshua Pilipovsky; Panagiotis Tsiotras

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arXiv · 2310.02485

Computationally Efficient Chance Constrained Covariance Control with Output Feedback

Joshua Pilipovsky·Panagiotis Tsiotras

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Abstract

This paper studies the problem of developing computationally efficient solutions for steering the distribution of the state of a stochastic, linear dynamical system between two boundary Gaussian distributions in the presence of chance-constraints on the state and control input. It is assumed that the state is only partially available through a measurement model corrupted with noise. The filtered state is reconstructed with a Kalman filter, the chance constraints are reformulated as difference of convex (DC) constraints, and the resulting covariance control problem is reformulated as a DC program, which is solved using successive convexification. The efficiency of the proposed method is illustrated on a double integrator example with varying time horizons, and is compared to other state-of-the-art chance constrained covariance control methods.

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BibTeX RIS

Joshua Pilipovsky, Panagiotis Tsiotras. 2023-10-03. Computationally Efficient Chance Constrained Covariance Control with Output Feedback. https://arxiv.org/abs/2310.02485

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