Decentralized reinforcement learning optimal control for time varying constrained reconfigurable modular robot

Abstract

Based on Action-Critic-Identifier(ACI)and Radial Basis Function(RBF)neural network,a novel decentralized reinforcement learning optimal control method for time varying constrained reconfigurable modular robot is presented.The continuous time nonlinear optimal control problem of strongly coupled uncertainty robotic system is solved.The dynamics of the robot is described as a synthesis of interconnected subsystems.As a precondition to the continuous-time MDPs performance indicators,the optimal value function,optimal control policy and global uncertainty of the subsystems are estimated combing with ACI and RBF network.The optimal conditions of HJB equation with regard to the subsystem are satisfied,so that the reconfigurable modular robot system can track the desired trajectory in a short time and the estimation error can converge to zero in finite time.The stability of the system is confirmed by Lyapunov theory.Simulations are performed to illustrate the effectiveness of the proposed decentralized control scheme.