Stackelberg Differential Game-based Hierarchical Approximate Optimal Interaction Control of Human-Centered Reconfigurable Robot Manipulator Systems

This paper proposes a Stackelberg game-based approximate optimal interaction control approach for human-centered reconfigurable robot manipulator (RRM) systems. Joint torque feedback technique is utilized to form the RRM dynamic model. According to differential game, the RRM’s optimal control issue is transformed into a Stackelberg game issue between the human and the RRM. By the adaptive dynamic programming (ADP) algorithm, the cost functions of human as well as RRM are developed by critic neural network (NN) and implement to solve Hamilton-Jacobian (HJ) equations, which facilitate the acquisition of Stackelberg equilibrium. The position tracking error is ultimately uniformly bounded (UUB) according to the Lyapunov theory. Experiment results demonstrate the effectiveness of the proposed method.