Data-Driven Predictive Control with Adaptive Disturbance Attenuation for Constrained Systems

TL;DR

This paper introduces a data-driven predictive control method that adaptively combines H-infinity disturbance rejection with MPC to handle constraints and disturbances in systems, ensuring stability and feasibility.

Contribution

It presents a novel adaptive control approach that integrates H-infinity disturbance attenuation with MPC, providing theoretical guarantees and handling noisy data.

Findings

Ensures robust closed-loop stability under disturbances.

Guarantees constraint satisfaction with noisy data.

Demonstrates effectiveness through a numerical example.

Abstract

In this paper, we propose a novel data-driven predictive control approach for systems subject to time-domain constraints. The approach combines the strengths of H-infinity control for rejecting disturbances and MPC for handling constraints. In particular, the approach can dynamically adapt H-infinity disturbance attenuation performance depending on measured system state and forecasted disturbance level to satisfy constraints. We establish theoretical properties of the approach including robust guarantees of closed-loop stability, disturbance attenuation, constraint satisfaction under noisy data, as well as sufficient conditions for recursive feasibility, and illustrate the approach with a numerical example.