Event-Triggered Robust MPC With Terminal Inequality Constraints: A Data-Driven Approach

An event-triggered robust model predictive control (MPC) design is proposed for unknown systems using initially measured input-output data. A terminal inequality constraint is developed for the MPC optimization problem without any prior identification, resulting in a larger feasible region and a lower bound for the prediction horizon when compared with a terminal equality constraint. An event-triggered scheme associated with a local controller is designed to trigger the solution of the data-driven MPC optimization problem when necessary, leading to the reduction of resource consumption. Under mild conditions, recursive feasibility and input-to-state stability are guaranteed theoretically. Simulation results are provided to show the effectiveness of the proposed approach.