Data-Driven Predictive Control with Adaptive Disturbance Attenuation for Constrained Systems
<|reference_start|>Data-Driven Predictive Control with Adaptive Disturbance Attenuation for Constrained Systems: 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.<|reference_end|>
@article{li2024data-driven,
    title={Data-Driven Predictive Control with Adaptive Disturbance Attenuation for
  Constrained Systems},
    author={Nan Li, Ilya Kolmanovsky, Hong Chen},
    journal={arXiv preprint arXiv:2403.14935},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14935},
    primaryClass={math.OC cs.SY eess.SY}
}