This paper presents a data-driven min-max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data collection and online system operation. Assuming a quadratic matrix inequality (QMI) description for the scheduling signal, we develop a novel data-driven characterization of the consistent system matrices using only input-state data. The proposed data-driven min-max MPC minimizes a tractable upper bound on the worst-case cost over the consistent system matrices set and over all scheduling signals satisfying the QMI. The proposed approach guarantees recursive feasibility, closed-loop exponential stability and constraint satisfaction if it is feasible at the initial time. We demonstrate the effectiveness of the proposed method in simulation.
@article{arxiv.2411.05624,
title = {Data-Driven Min-Max MPC for LPV Systems with Unknown Scheduling Signal},
author = {Yifan Xie and Julian Berberich and Felix Brändle and Frank Allgöwer},
journal= {arXiv preprint arXiv:2411.05624},
year = {2024}
}