Optimal Covariance Steering of Linear Stochastic Systems with Hybrid Transitions

TL;DR

This paper develops a convex optimization framework for optimal covariance steering in linear stochastic hybrid systems, addressing jump dynamics, uncertainties, and singularities with analytical and reformulated solutions.

Contribution

It introduces a novel convex approach to the hybrid covariance steering problem, including analytical solutions for nonsingular jumps and reformulation via Schrödinger's Bridge for complex cases.

Findings

Analytical closed-form solution for nonsingular jump dynamics.

Convex reformulation using Schrödinger's Bridge duality.

Efficient linear-scaling algorithm for hybrid covariance control.

Abstract

This work addresses the problem of optimally steering the state covariance of a linear stochastic system from an initial to a target, subject to hybrid transitions. The nonlinear and discontinuous jump dynamics complicate the control design for hybrid systems. Under uncertainties, stochastic jump timing and state variations further intensify this challenge. This work aims to regulate the hybrid system's state trajectory to stay close to a nominal deterministic one, despite uncertainties and noises. We address this problem by directly controlling state covariances around a mean trajectory, and this problem is termed the Hybrid Covariance Steering (H-CS) problem. The jump dynamics are approximated to the first order by leveraging the Saltation Matrix. When the jump dynamics are nonsingular, we derive an analytical closed-form solution to the H-CS problem. For general jump dynamics with possible singularity and changes in the state dimensions, we reformulate the problem into a convex optimization over path distributions by leveraging Schrodinger's Bridge duality to the smooth covariance control problem. The covariance propagation at hybrid events is enforced as equality constraints to handle singularity issues. The proposed convex framework scales linearly with the number of jump events, ensuring efficient, optimal solutions. This work thus provides a computationally efficient solution to the general H-CS problem. Numerical experiments are conducted to validate the proposed method.