Exact SDP Formulation for Discrete-Time Covariance Steering with Wasserstein Terminal Cost

TL;DR

This paper introduces an SDP-based approach for discrete-time covariance steering with Wasserstein terminal cost, replacing complex control policies with a tractable randomized policy that maintains optimality.

Contribution

It proposes a simplified randomized state feedback policy, reducing variable complexity and enabling an SDP formulation for covariance steering with Wasserstein cost.

Findings

SDP formulation achieves computational efficiency

Randomized policy matches deterministic policy performance

Numerical results outperform existing methods

Abstract

In this paper, we present new results on the covariance steering problem with Wasserstein distance terminal cost. We show that the state history feedback control policy parametrization, which has been used before to solve this class of problems, requires an unnecessarily large number of variables and can be replaced by a randomized state feedback policy which leads to more tractable problem formulations without any performance loss. In particular, we show that under the latter policy, the problem can be equivalently formulated as a semi-definite program (SDP) which is in sharp contrast with our previous results that could only guarantee that the stochastic optimal control problem can be reduced to a difference of convex functions program. Then, we show that the optimal policy that is found by solving the associated SDP corresponds to a deterministic state feedback policy. Finally, we present non-trivial numerical simulations which show the benefits of our proposed randomized state feedback policy derived from the SDP formulation of the problem over existing approaches in the field in terms of computational efficacy and controller performance.