Neural network based tracking control for an elastic joint robot with input constraint via actor-critic design

In this paper, adaptive control with actor-critic design is proposed for an elastic joint robot to cope with the tracking problems. In the critic network, a cost function is used to judge the control performance. Since it is unmeasurable and cannot be calculated, a neural network is utilized to approximate it. In the actor network, based on the critic results as the reinforcement signal, another neural network is used to cope with the system uncertainties and generate the control input. Furthermore, input constraint is imposed on the elastic joint robot to guarantee the normal operation. A high gain observer is also adopted to estimate the unmeasurable variables. With the given adaptive laws of the critic neural network and actor neural network, through the Lyapunov direct method, the stability and convergence of the closed-loop system are achieved and the tracking errors are ultimately uniformly bounded. In addition, simulations are also conducted on a two-link elastic joint robot to illustrate the feasibility and effectiveness of the proposed adaptive constrained actor-critic learning based control. (C) 2020 Published by Elsevier B.V.