By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system.
@article{arxiv.2311.07140,
title = {A Linear Parameter-Varying Approach to Data Predictive Control},
author = {Chris Verhoek and Julian Berberich and Sofie Haesaert and Roland Tóth and Hossam S. Abbas},
journal= {arXiv preprint arXiv:2311.07140},
year = {2026}
}
Comments
To appear in IEEE Transactions on Automatic Control. Final Author Copy. Extended version (Section VI.C, Appendix C & D not in original version). 18 pages