Fault Tolerant Control for Reconfigurable Manipulators Based on Adaptive Dynamic Programming with an Improved Performance Index Function

This paper investigates a novel fault tolerant control (FTC) scheme for reconfigurable manipulators with actuator failures based on adaptive dynamic programming (ADP) with an improved performance index function. With the help of fault observer, the estimated actuator failure is utilized to construct the performance index function that reflects the failure, regulation and control simultaneously. The fault tolerant controller consists of the desired controller and the approximated optimal feedback one. The desired controller is obtained by the system model directly. By establishing a critic neural network, the policy iteration (PI) algorithm is utilized to solve the Hamilton-Jacobi-Bellman (HJB) equation, and then the approximated optimal feedback controller can be derived directly. It is proven that the closed-loop system is uniformly ultimately bounded (UUB) based on Lyapunov stability theorem. Finally, the simulation study is given to show the effectiveness of the proposed FTC scheme.