Computationally Efficient Chance Constrained Covariance Control with Output Feedback

TL;DR

This paper presents a computationally efficient method for covariance control of stochastic linear systems with output feedback, reformulating chance constraints as DC programs and solving via successive convexification.

Contribution

It introduces a novel approach that combines Kalman filtering, DC reformulation, and convexification to efficiently solve chance-constrained covariance control problems.

Findings

Method outperforms existing approaches in efficiency.

Successfully applied to a double integrator example.

Demonstrates scalability with varying time horizons.

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.