Optimal Sliding Mode Tracking Control for Reconfigurable Manipulators Based on Adaptive Dynamic Programming

Abstract

This paper presents a novel optimal sliding mode tracking control method for reconfigurable manipulators based on the policy iteration (PI) and adaptive dynamic programming (ADP). The designed sliding mode tracking control can suppress the tracking error caused by reconfiguration of the manipulators. Based on ADP and PI algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic neural network and then the approximated optimal sliding mode control policy can be derived directly. Based on the Lyapunov stability theorem, the closed-loop robotic system is proved to be asymptotic stability. Finally, simulations are provided to demonstrate the effectiveness of the proposed method.