Data-driven moving-horizon control with adaptive disturbance attenuation for constrained systems

In this paper, we propose a novel data-driven moving-horizon control approach for systems subject to time-domain constraints. The approach combines the strengths of $H_{\infty }$ control for rejecting disturbances and MPC for handling constraints. In particular, the approach can dynamically adapt $H_{\infty }$ 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.