We investigate nonlinear model predictive control (MPC) with terminal conditions in the Koopman framework using extended dynamic mode decomposition (EDMD) to generate a data-based surrogate model for prediction and optimization. We rigorously show recursive feasibility and prove practical asymptotic stability w.r.t. the approximation accuracy. To this end, finite-data error bounds are employed. The construction of the terminal conditions is based on recently derived proportional error bounds to ensure the required Lyapunov decrease. Finally, we illustrate the effectiveness of the proposed data-driven predictive controller including the design procedure to construct the terminal region and controller.
@article{arxiv.2408.12457,
title = {Data-driven MPC with terminal conditions in the Koopman framework},
author = {Karl Worthmann and Robin Strässer and Manuel Schaller and Julian Berberich and Frank Allgöwer},
journal= {arXiv preprint arXiv:2408.12457},
year = {2025}
}
Comments
Accepted for presentation at the 63rd IEEE Conference on Decision and Control (CDC2024)