Robust event-triggered model predictive control for constrained linear continuous system

Model predictive control (MPC) is capable to deal with multiconstraint systems in real control processes; however, the heavy computation makes it difficult to implement. In this paper, a dual-mode control strategy based on event-triggered MPC (ETMPC) and state-feedback control for continuous linear time-invariant systems including control input constraints and bounded disturbances is developed. First, the deviation between the actual state trajectory and the optimal state trajectory is computed to set an event-triggered mechanism and reduce the computational load of MPC. Next, the dual-mode control strategy is designed to stabilize the system. Both recursive feasibility and stability of the strategy are guaranteed by constructing a feasible control sequence and deducing the relationship of parameters, especially the inter-event time and the upper bound of the disturbances. Finally, the theoretical results are supported by numerical simulation. In addition, the effects of the parameters are discussed by simulation, which gives guidance to balance computational load and control performance.