A novel model predictive control- (MPC-) based trajectory tracking controller for mobile robot is proposed using the event-triggering mechanism, and the aim is to solve the problem that the MPC optimization problem requires a large amount of online computation and communication resources. This method includes two different event-triggering strategies, namely, the event-triggering based on threshold curve and the event-triggering based on threshold band. The selection of the triggering threshold is achieved by applying the statistical method to the historical data of the trajectory tracking of the mobile robot under the classic MPC method. Simulation and experimental tests illustrate that the proposed approach is able to significantly reduce the computation and communication burdens without affecting the control performance. Furthermore, the experimental results show that compared with the classic MPC-based tracking method, the proposed two event-triggering strategies can reduce 28.1% and 75.7% of the computation load and 0.886 s and 2.385 s communication time.

Aiming at solving the control problem of a constrained and perturbed underwater robot, a control method was proposed by combining the self-triggered mechanism and the nonlinear model predictive control (NMPC). The theoretical properties of the kinematic model of the underwater robot, as well as the corresponding MPC controller, are first studied. Then, a novel technique for determining the next update moment of both the optimal control problem and the system state is developed. It is further rigorously proved that the proposed algorithm can (1) stabilize the closed-loop underwater robot system, (2) reduce the time of solving the optimal control problem and (3) save the information transfer resources. Finally, a case study is provided to show the effectiveness of the developed researched scheme.

A novel event-driven model predictive control (MPC) technique is constructed for perturbed nonlinear system including both input and state constraints. The primary merit of the developed method is the event-driven input signal is constructed based on sparse control samples, and only the selected samples, rather than a continuous control signal, need to be transmitted through the network. Given such a framework, a tightened constraint is designed to satisfy the robust requirement, and an event-driven scheme is designed to lessen the computational as well as communication load of the considered MPC system. Then, theoretical requirements for guaranteeing the MPC feasibility and system convergence are figured out. Finally, proposed input signal reconstruction based MPC method is tested via simulation experiments as well as comparative study.INDEX TERMS Event-driven control, model predictive control (MPC), constrained systems, input signal reconstruction.