On the design of regularized explicit predictive controllers from input-output data

TL;DR

This paper introduces a regularized explicit predictive control method derived solely from input-output data, enhancing noise robustness and ensuring uniqueness, with demonstrated effectiveness on benchmark simulations.

Contribution

It presents a novel regularized data-driven explicit predictive controller that guarantees uniqueness and improves noise handling, advancing data-driven control design.

Findings

Regularization guarantees the uniqueness of the explicit controller.

The method improves noise robustness in data-driven predictive control.

Benchmark simulations confirm the effectiveness of the proposed approach.

Abstract

On the wave of recent advances in data-driven predictive control, we present an explicit predictive controller that can be constructed from a batch of input/output data only. The proposed explicit law is build upon a regularized implicit data-driven predictive control problem, so as to guarantee the uniqueness of the explicit predictive controller. As a side benefit, the use of regularization is shown to improve the capability of the explicit law in coping with noise on the data. The effectiveness of the retrieved explicit law and the repercussions of regularization on noise handling are analyzed on two benchmark simulation case studies, showing the potential of the proposed regularized explicit controller.