Data-driven $H_{\infty}$ predictive control for constrained systems: a Lagrange duality approach

TL;DR

This paper introduces a data-driven $H_{ {infty}}$ predictive control method that combines minimax MPC and Lagrange duality to effectively handle disturbances and constraints in systems, validated through simulations.

Contribution

It presents a novel data-driven control scheme integrating $H_{ {infty}}$ control with minimax MPC using Lagrange duality to reduce conservatism and improve constraint handling.

Findings

Ensures $H_{ {infty}}$ performance using data-driven methods.

Reduces conservatism in constraint evaluation via Lagrange duality.

Demonstrates robustness and feasibility through numerical simulations.

Abstract

This article proposes a data-driven $H_{\infty}$ control scheme for time-domain constrained systems based on model predictive control formulation. The scheme combines $H_{\infty}$ control and minimax model predictive control, enabling more effective handling of external disturbances and time-domain constraints. First, by leveraging input-output-disturbance data, the scheme ensures $H_{\infty}$ performance of the closed-loop system. Then, a minimax optimization problem is converted into a more manageable minimization problem employing Lagrange duality, which reduces conservatism typically associated with ellipsoidal evaluations of time-domain constraints. The study examines key closed-loop properties, including stability, disturbance attenuation, and constraint satisfaction, achieved by the proposed data-driven moving horizon predictive control algorithm. The effectiveness and advantages of the proposed method are demonstrated through numerical simulations involving a batch reactor system, confirming its robustness and feasibility under noisy conditions.