Robust Output Feedback MPC with Reduced Conservatism under Ellipsoidal Uncertainty

TL;DR

This paper introduces a less conservative robust output feedback MPC method for linear systems with ellipsoidal uncertainty, improving scalability and constraint satisfaction without over-approximation.

Contribution

It presents a novel tube-based predictive control approach that directly computes constraint tightening from ellipsoidal sets, avoiding Minkowski sums and enhancing scalability.

Findings

Reduced conservatism in constraint tightening

Applicable to high-dimensional systems

Validated through illustrative examples

Abstract

Robust design of autonomous systems under uncertainty is an important yet challenging problem. This work proposes a robust controller that consists of a state estimator and a tube based predictive control law. The class of linear systems under ellipsoidal uncertainty is considered. In contrast to existing approaches based on polytopic sets, the constraint tightening is directly computed from the ellipsoidal sets of disturbances without over-approximation, thus leading to less conservative bounds. Conditions to guarantee robust constraint satisfaction and robust stability are presented. Further, by avoiding the usage of Minkowski sum in set computation, the proposed approach can also scale up to high-dimensional systems. The results are illustrated by examples.