Robust Closed-loop Model Predictive Control via System Level Synthesis

TL;DR

This paper introduces a robust closed-loop MPC approach using System Level Synthesis that effectively manages disturbances and model uncertainties, reducing conservativeness and improving computational efficiency.

Contribution

It reformulates robust constrained optimal control problems within the SLS framework, enabling transparent handling of uncertainties and enhancing robustness with less conservativeness.

Findings

Reduced conservativeness compared to existing methods

Improved computational efficiency in robust control design

Effective handling of disturbances and model uncertainties

Abstract

In this paper, we consider the robust closed-loop model predictive control (MPC) of a linear time-variant (LTV) system with norm bounded disturbances and LTV model uncertainty, wherein a series of constrained optimal control problems (OCPs) are solved. Guaranteeing robust feasibility of these OCPs is challenging due to disturbances perturbing the predicted states, and model uncertainty, both of which can render the closed-loop system unstable. As such, a trade-off between the numerical tractability and conservativeness of the solutions is often required. We use the System Level Synthesis (SLS) framework to reformulate these constrained OCPs over closed-loop system responses, and show that this allows us to transparently account for norm bounded additive disturbances and LTV model uncertainty by computing robust state feedback policies. We further show that by exploiting the underlying linear fractional structure of the resulting robust OCPs, we can significantly reduce the conservativeness of existing SLS-based and tube-MPC-based robust control methods while also improving computational efficiency. We conclude with numerical examples demonstrating the effectiveness of our methods.