Event‐trigger‐based approximate optimal control of modular robot manipulators using zero‐sum game

Abstract

Abstract An event‐triggered‐based approximate optimal control is developed for modular robot manipulators (MRMs) using zero‐sum game strategy. By utilizing the joint torque feedback method, robotic systems' dynamic model is formulated and state space equation is derived. According to event‐triggered mechanism and neural network algorithm, the tracking control issue is converted to a zero‐sum game optimal control issue. The optimal control policy and worst disturbance policy are acquired by Hamilton–Jacobi–Isaacs function respectively. The MRM system's tracking error is ultimately uniformly bounded. Finally, experiments demonstrate advantages of the method.