Novel event-triggered strategies for Model Predictive Controllers

This paper proposes novel event-triggered strategies for the control of uncertain nonlinear systems with additive disturbances under robust Nonlinear Model Predictive Controllers (NMPC). The main idea behind the event-driven framework is to trigger the solution of the optimal control problem of the NMPC, only when it is needed. The updates of the control law depend on the error of the actual and the predicted trajectory of the system. Sufficient conditions for triggering are provided for both, continuous and discrete-time nonlinear systems. The closed-loop system evolves to a compact set where it is ultimately bounded, under the proposed framework. The results are illustrated through a simulated example.