In this paper, we propose a novel data-driven predictive control approach for systems subject to time-domain constraints. The approach combines the strengths of H-infinity control for rejecting disturbances and MPC for handling constraints. In particular, the approach can dynamically adapt H-infinity disturbance attenuation performance depending on measured system state and forecasted disturbance level to satisfy constraints. We establish theoretical properties of the approach including robust guarantees of closed-loop stability, disturbance attenuation, constraint satisfaction under noisy data, as well as sufficient conditions for recursive feasibility, and illustrate the approach with a numerical example.
@article{arxiv.2403.14935,
title = {Data-Driven Predictive Control with Adaptive Disturbance Attenuation for Constrained Systems},
author = {Nan Li and Ilya Kolmanovsky and Hong Chen},
journal= {arXiv preprint arXiv:2403.14935},
year = {2024}
}