Aperiodic intermittent sampling for nonlinear discrete-time event-based model predictive control

In this article, an aperiodic intermittent sampling method is designed in event-based model predictive control for nonlinear discrete systems subject to external disturbances. The proposed aperiodic intermittent sampling is applied to find the appropriate triggering instant satisfying the desired sub-optimal performance. In the proposed event-based MPC, selection of the sampling instants is based on the prediction of the worst case induced by disturbances and the mapping principle with updated state feedback. With the proposed event-based MPC, the measurement frequency and the triggering rate can be reduced by sampling the system states only at certain time instants. Recursive feasibility and robust stability are established under sufficient conditions.