We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a terminal cost and a terminal set constraint on an extended state containing past input-output values. We provide an explicit design procedure for the corresponding terminal ingredients that only uses measured input-output data. Further, we prove that the MPC scheme based on these terminal ingredients exponentially stabilizes the desired setpoint in closed loop. Finally, we illustrate the advantages over existing data-driven MPC approaches with a numerical example.
@article{arxiv.2101.05573,
title = {On the design of terminal ingredients for data-driven MPC},
author = {Julian Berberich and Johannes Köhler and Matthias A. Müller and Frank Allgöwer},
journal= {arXiv preprint arXiv:2101.05573},
year = {2022}
}
Comments
Final version, accepted for presentation at the 7th IFAC Conference on Nonlinear Model Predictive Control 2021