Covariance Steering of Discrete-Time Linear Systems with Mixed Multiplicative and Additive Noise

TL;DR

This paper addresses the covariance steering problem for discrete-time linear systems with both multiplicative and additive noise, proposing convex optimization solutions for exact and relaxed formulations, validated through numerical experiments.

Contribution

It introduces a convex SDP approach for the relaxed covariance steering problem and a two-step method for the exact problem, handling mixed noise types.

Findings

Convex SDP formulation for the relaxed covariance steering problem.

A two-step solution method for the exact covariance steering problem.

Numerical results demonstrating the effectiveness of the proposed methods.

Abstract

In this paper, we study the covariance steering (CS) problem for discrete-time linear systems subject to multiplicative and additive noise. Specifically, we consider two variants of the so-called CS problem. The goal of the first problem, which is called the exact CS problem, is to steer the mean and the covariance of the state process to their desired values in finite time. In the second one, which is called the ``relaxed'' CS problem, the covariance assignment constraint is relaxed into a positive semi-definite constraint. We show that the relaxed CS problem can be cast as an equivalent convex semi-definite program (SDP) after applying suitable variable transformations and constraint relaxations. Furthermore, we propose a two-step solution procedure for the exact CS problem based on the relaxed problem formulation which returns a feasible solution, if there exists one. Finally, results from numerical experiments are provided to show the efficacy of the proposed solution methods.