Reinforcement Learning Control for Manipulation of Flexible Payloads by Multiagent Robot Systems With Event Triggering Mechanism

This study focuses on the reinforcement learning (RL)-based consensus tracking control of nonlinear multiagent robot systems (MARSs) with event triggering mechanism. Each agent of the MARSs is composed of a three-link rigid robot and a flexible payload, which can be assumed to be a Eulbernoulli beam. Based on the assumed mode method (AMM), the infinite distributed parameter model of the robot–payload system is approximated as a finite dimension model, and the dynamic performance of the robot system is controlled with the use of boundary control input. First, a RL control strategy based on actor–critic structure is adopted to maintain the consensus angles tracking of all agents while suppress the load vibration. Second, considering the communication bandwidth problem in practical applications, an event-triggered mechanism is utilized to reduce the transmission burden based on relative threshold strategy. Furthermore, the semi-global uniformly ultimately bounded (SGUUB) property of the closed-loop system is derived to guarantee the state errors can converge to the small neighborhoods of the origin. Finally, the effectiveness of the proposed control strategy is demonstrated by numerical simulations.