Robust multi-rate predictive control using multi-step prediction models learned from data

TL;DR

This paper introduces a robust multi-rate predictive control method that uses multi-step prediction models learned from data, aiming to improve constraint enforcement and stability in linear systems with disturbances.

Contribution

It develops a new multi-rate robust MPC algorithm utilizing least conservative multi-step predictors learned via Set Membership methods.

Findings

Prediction error bounds are least conservative within the model class.

The proposed MPC reduces conservativeness while maintaining robustness.

Simulation demonstrates improved control performance.

Abstract

This note extends a recently proposed algorithm for model identification and robust MPC of asymptotically stable, linear time-invariant systems subject to process and measurement disturbances. Independent output predictors for different steps ahead are estimated with Set Membership methods. It is here shown that the corresponding prediction error bounds are the least conservative in the considered model class. Then, a new multi-rate robust MPC algorithm is developed, employing said multi-step predictors to robustly enforce constraints and stability against disturbances and model uncertainty, and to reduce conservativeness. A simulation example illustrates the effectiveness of the approach.