Data-driven model predictive control: closed-loop guarantees and experimental results

TL;DR

This paper reviews and demonstrates a data-driven model predictive control framework that guarantees stability for unknown linear and nonlinear systems using only measured data, with practical experiments validating its effectiveness.

Contribution

It introduces a novel data-driven MPC approach based on behavioral systems theory that ensures closed-loop stability without explicit models, extending to nonlinear systems with real-time data updates.

Findings

Guarantees stability for unknown LTI systems with noisy data.

Successfully applies to nonlinear systems with real-time data updates.

Validated through simulation and experimental results on a nonlinear four-tank system.

Abstract

We provide a comprehensive review and practical implementation of a recently developed model predictive control (MPC) framework for controlling unknown systems using only measured data and no explicit model knowledge. Our approach relies on an implicit system parametrization from behavioral systems theory based on one measured input-output trajectory. The presented MPC schemes guarantee closed-loop stability for unknown linear time-invariant (LTI) systems, even if the data are affected by noise. Further, we extend this MPC framework to control unknown nonlinear systems by continuously updating the data-driven system representation using new measurements. The simple and intuitive applicability of our approach is demonstrated with a nonlinear four-tank system in simulation and in an experiment.