Adaptive Dynamic Programming-Based Fault-Tolerant Position-Force Control of Constrained Reconfigurable Manipulators

This article presents a novel fault-tolerant position-force optimal control method for constrained reconfigurable manipulators with uncertain actuator failures. On the basis of the radial basis function neural network (RBFNN)-estimated manipulators dynamics, the proposed force-position error fusion function and the estimated actuator failure are utilized to construct an improved optimal performance index function, which reflects the faults and optimizes system comprehensive performance as well as the energy consumption simultaneously. Based on the policy iteration (PI) scheme and the adaptive dynamic programming (ADP) algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation is solved by constructing the critic neural network (NN), and then the approximated fault-tolerant position-force optimal control policy can be derived correspondingly. The closed-loop manipulator system is proved to be asymptotically stable by using the Lyapunov theory. Finally, simulations are provided to demonstrate the effectiveness of the proposed method.