Adaptive MPC for Iterative Tasks

TL;DR

This paper introduces an adaptive MPC method for uncertain linear systems performing iterative tasks, which iteratively refines the uncertainty domain to improve control performance and reduce conservatism.

Contribution

The paper presents a novel adaptive MPC approach that updates the offset uncertainty domain iteratively, enhancing performance over classical robust MPC methods.

Findings

Reduces conservatism compared to classical robust MPC.

Improves trajectory tracking with lower costs over iterations.

Numerical simulations demonstrate enhanced control performance.

Abstract

This paper proposes an Adaptive Learning Model Predictive Control strategy for uncertain constrained linear systems performing iterative tasks. The additive uncertainty is modeled as the sum of a bounded process noise and an unknown constant offset. As new data becomes available, the proposed algorithm iteratively adapts the believed domain of the unknown offset after each iteration. An MPC strategy robust to all feasible offsets is employed in order to guarantee recursive feasibility. We show that the adaptation of the feasible offset domain reduces conservatism of the proposed strategy, compared to classical robust MPC strategies. As a result, the controller performance improves. Performance is measured in terms of following trajectories with lower associated costs at each iteration. Numerical simulations highlight the main advantages of the proposed approach.