Decentralized position-force zero-sum approximate optimal control for reconfigurable robots with unmodeled dynamic

In this paper, the position-force-based approximate optimal control method is developed for reconfigurable robots using zero-sum game strategy. By utilizing the Newton-Euler iteration technique, the robotic system's dynamic model is formulated and the state space equation is derived. According to adaptive dynamic programming (ADP) and neural network algorithm, the trajectory tracking control problem is transformed into a zero-sum game-based optimal control issue. The optimal control policy and worst disturbance policy are obtained by Hamilton-Jacobi-Issacs (HJI) function, respectively. Unlike the conventional learning-based robotic control method, the proposed zero-sum game-based method no need extra sub-controller that can reduce the computational burden. The reconfigurable robot system's tracking error is uniformly ultimately bounded by the Lyapunov theorem. Finally, simulation experiments demonstrate the advantages of the proposed method.