Distributional Robustness in Output Feedback Regret-Optimal Control

TL;DR

This work addresses distributionally robust regret-optimal control (DRRO) for linear systems with purified output feedback under Wasserstein ambiguity sets. By deriving strong duality results for worst-case expectations with quadratic objectives, the inner maximisation in DRRO is reformulated as semidefinite programs (SDPs). The authors then apply variable elimination via the Projection Lemma to reduce the SDP size and present a distributed reformulation with consensus constraints to improve scalability. A numerical mass-spring example demonstrates comparable performance between the original and reduced formulations while achieving substantial computational savings, highlighting the practical potential for large-scale DRRO design.

Abstract

This paper studies distributionally robust regret-optimal (DRRO) control with purified output feedback for linear systems subject to additive disturbances and measurement noise. These uncertainties (including the initial system state) are assumed to be stochastic and distributed according to an unknown joint probability distribution within a Wasserstein ambiguity set. We design affine controllers to minimise the worst-case expected regret over all distributions in this set. The expected regret is defined as the difference between an expected cost incurred by an affine causal controller and the expected cost incurred by the optimal noncausal controller with perfect knowledge of the disturbance trajectory at the outset. Leveraging the duality theory in distributionally robust optimisation, we derive strong duality results for worst-case expectation problems involving general quadratic objective functions, enabling exact reformulations of the DRRO control problem as semidefinite programs (SDPs). Focusing on one such reformulation, we eliminate certain decision variables. This technique also permits a further equivalent reformulation of the SDP as a distributed optimisation problem, with potential to enhance scalability.