Optimal tracking control for reconfigurable manipulators based on critic-only policy iteration algorithm

This paper tackles the optimal tracking control problem for reconfigurable manipulators based on critic-only policy iteration (CoPI) algorithm. By system transformation, the optimal tracking control problem is transformed into an optimal regulation problem. The optimal tracking controller is composed of the desired controller and the approximate optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, while the approximate optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. Then, a critic neural network is used to estimate the optimal performance index function, and the optimal feedback control is obtained by the CoPI algorithm. The convergence of the proposed method is analyzed and it is shown that the closed-loop system based on CoPI is uniformly ultimately bounded by using the Lyapunov approach. Finally, simulation studies are given to show the effectiveness of the developed method.