Event-Based Distributed Predictive Approach for the Cooperation of Networked Mobile Manipulators

This article presents an event-triggered distributed model predictive control strategy for the cooperation of networked mobile manipulators to save communication and computational resources effectively. First, based on the nonlinear feedback technique, an auxiliary model with bounded disturbances is established, and a model predictive controller is designed for each mobile manipulator considering the system constraints. Then, an event-triggering mechanism involving the difference between the actual state and its optimal prediction is developed to reduce the frequency of communication and computation of the networked mobile manipulators. The optimization problem is solved, and information is exchanged only when the event-triggering condition is satisfied. Moreover, sufficient conditions for guaranteeing the feasibility of the proposed algorithm and the stability of the closed-loop system are formulated, respectively. Finally, the simulation results show that the proposed strategy works well.