Robust Control of Constrained Linear Systems using Online Convex Optimization and a Reference Governor

TL;DR

This paper presents a robust control framework for constrained linear systems that integrates online convex optimization with a reference governor, ensuring constraint satisfaction and bounded regret despite disturbances and unknown cost functions.

Contribution

It introduces a novel combination of online convex optimization and reference governors for robust, constraint-aware control of linear systems with unknown, time-varying costs.

Findings

Guarantees recursive feasibility and robust constraint satisfaction.

Bounded dynamic regret proportional to cost variation and disturbance magnitude.

Validated effectiveness through a numerical tracking control case study.

Abstract

This article develops a control method for linear time-invariant systems subject to time-varying and a priori unknown cost functions, that satisfies state and input constraints, and is robust to exogenous disturbances. To this end, we combine the online convex optimization framework with a reference governor and a constraint tightening approach. The proposed framework guarantees recursive feasibility and robust constraint satisfaction. Its closed-loop performance is studied in terms of its dynamic regret, which is bounded linearly by the variation of the cost functions and the magnitude of the disturbances. The proposed method is illustrated by a numerical case study of a tracking control problem.