Multi-Objective Learning Model Predictive Control

TL;DR

This paper introduces a data-driven control scheme that iteratively improves a linear system's performance across multiple objectives, ensuring convergence to a Pareto optimal policy through recursive feasibility and performance guarantees.

Contribution

It presents a novel Multi-Objective Learning Model Predictive Control framework that guarantees performance improvement and Pareto optimality in iterative control tasks.

Findings

Performance improves over iterations for all objectives.

The control policy converges to Pareto optimality.

Simulation demonstrates effectiveness of the approach.

Abstract

Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a linear system's closed-loop performance with respect to several convex control objectives over iterations of a repeated task. At each task iteration, collected system data is used to construct terminal components of a Model Predictive Controller. The formulation presented in this paper ensures that closed-loop control performance improves between successive iterations with respect to each objective. We provide proofs of recursive feasibility and performance improvement, and show that the converged policy is Pareto optimal. Simulation results demonstrate the applicability of the proposed approach.