We show that the explicit realization of data-driven predictive control (DPC) for linear deterministic systems is more tractable than previously thought. To this end, we compare the optimal control problems (OCP) corresponding to deterministic DPC and classical model predictive control (MPC), specify its close relation, and systematically eliminate ambiguity inherent in DPC. As a central result, we find that the explicit solutions to these types of DPC and MPC are of exactly the same complexity. We illustrate our results with two numerical examples highlighting features of our approach.
@article{arxiv.2206.07025,
title = {A deterministic view on explicit data-driven (M)PC},
author = {Manuel Klädtke and Dieter Teichrib and Nils Schlüter and Moritz Schulze Darup},
journal= {arXiv preprint arXiv:2206.07025},
year = {2023}
}
Comments
7 pages, 2 figure, submitted to 61st IEE Conference on Decision and Control 2022