Compensator-critic structure-based neuro-optimal control of modular robot manipulators with uncertain environmental contacts using non-zero-sum games

摘要

In this paper, a novel compensator-critic structure-based neuro-optimal control approach is presented for modular robot manipulators (MRMs) in contact with uncertain environments. Based on joint torque feedback (JTF) technique, the dynamic model of the manipulator systems is described as an integration of joint subsystems associated with the effect of interconnected dynamic coupling (IDC). A local dynamic information-based robust compensator is designed to engage the model uncertainty compensation, and then, the optimal tracking control problem of an MRM system is transformed into an n-player non-zero-sum game issue of multiple joint subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a cost function approximator, which is constructed by using only critic neural networks (NNs), is developed for solving the Hamilton-Jacobi (HJ) equation, thus facilitating the feasible derivation of the neuro-optimal control policy. The tracking error of the closed-loop robotic system is proved to be uniformly ultimately bounded (UUB) on the basis of the Lyapunov theory. Finally, experiment results illustrated the advantage and effectiveness of the developed control method. (C) 2021 Elsevier B.V. All rights reserved.