Event-triggered model predictive control of discrete-time linear systems subject to disturbances

This paper presents an approach to event-triggered model predictive control for discrete-time linear systems subject to input and state constraints as well as exogenous disturbances. Stability properties are derived by evaluating the difference between the event-triggered implementation and the conventional time-triggered scheme. It is shown that the event-triggered implementation, in stationarity, is able to keep the state in an explicitly computable set given by the disturbance bound and the event threshold. Simulation results underline the effectiveness of the proposed scheme in terms of reducing the communication and computational effort while guaranteeing a desired performance.