Event-Based Robust Sampled-Data Model Predictive Control: A Non-Monotonic Lyapunov Function Approach

In this paper, two event-based robust sampled-data model predictive control (MPC) strategies are proposed based on the non-monotonic Lyapunov function approach for continuous-time systems with disturbances. Each event-triggering mechanism consists of the event-based MPC law and the triggering conditions. We show that although the proposed event-triggering conditions are only checked at the sampling instants and the control law is piecewise constant, the feasibility of the event-based sampled-data MPC algorithm and the stability of the closed-loop system are guaranteed in continuous time. Besides, the implementation issue is discussed, and we show that the proposed triggering conditions can be checked rapidly without obviously increasing the computational burden. Finally, an application to a nonholonomic robot system is provided to illustrate the effectiveness of the proposed results.