Optimized consensus control of multi-manipulator system having actuator fault using reinforcement learning approximation strategy

Abstract

This work is to develop an optimized consensus control of multi-manipulator system by employing reinforcement learning (RL) approximation strategy, while the multi-manipulator system is supposed to have the problem of actuator uncertain fault. The RL approximation strategy aims to avoid directly solving the Hamilton-Jacobi-Bellman (HJB) equation for finding the optimized consensus control because this equation has the strong nonlinearity. Since the manipulator system is modeled by the double-integral dynamic, the RL algorithm needs to simultaneously involve two position and velocity states. Meanwhile, it needs to also consider the compensation of actuator fault consisting of both time-varying efficiency factor and bias signal. By defining the performance function containing the above information, the optimized leader-follower consensus can be competent to work for the multi-manipulator system having actuator fault. Finally, the feasibility and validation of this optimizing consensus method is demonstrated from the two aspects of theory analysis and computer simulation.