Event‐triggered robust model predictive control for linear discrete‐time systems with a guaranteed average inter‐execution time

Abstract

AbstractAn event‐triggered robust model predictive control (MPC) approach is proposed for linear discrete‐time systems with bounded disturbances. According to the probability distribution of bounded disturbances, an event‐triggered scheme involving a designed minimal robust positively invariant set is constructed to generate dynamic triggering sets. The MPC‐related optimization problem subject to hard constraints should be solved only at event‐triggered instants when the state is outside the corresponding triggering set. A classical tube‐based MPC that allows the initially predicted state different from the current actual state of the plant is considered to improve the feasible region. The designed event‐triggered controller can achieve a prescribed expectation of inter‐execution times and reduce the burden of communication and computation, while not sacrificing the quadratic performance significantly. It is proved that the proposed control approach ensures recursive feasibility and robust stability. Three examples are used to demonstrate the effectiveness and advantages of the proposed method.