Integral-Type Event-Triggered Model Predictive Control for Manipulator Systems

In this paper, we study an integral-type event-triggered model predictive control method for manipulator systems. The event-triggered mechanism, which is based on integrating the error between the actual and predicted states, is applied to the system when the state is outside the terminal domain. In comparison to directly using the difference, the proposed method achieves a more effective balance between system performance and the frequency of solving optimization problems. Furthermore, the event-triggered strategy is also considered in the state-feedback controller to save computing resources. The feasibility and stability are rigorously analyzed. Finally, the simulation results of the manipulator system validate the feasibility and effectiveness of the proposed algorithm.