Model Free Optimal Integral Sliding Mode Control for Reconfigurable Manipulators Based on Adaptive Dynamic Programming

In this paper, a model-free integral sliding mode control method is presented via adaptive dynamic programming (ADP) to address the trajectory tracking problem of reconfigurable manipulators. The integral sliding mode control method is adopted to deal with the model uncertainties of joint subsystems and a local neural network identifier is designed to compensate the effect of the interconnection dynamic couplings (IDCs) between the subsystems. Based on the ADP and the policy iteration (PI) algorithm, the Hamilton-Jacobi-Bellman (HJB) equation can be solved by using critic neural network and then we can get the optimal control strategy. Based on the Lyapunov theory, the closed-loop manipulator system is proved to be asymptotic stability. Finally, simulation results are demonstrated the effectiveness of the proposed method.