Decentralized approximated optimal control for modular robot manipulations with physical human–robot interaction: a cooperative game-based strategy.

Major challenges of controlling physical human–robot interaction (pHRI)-oriented modular robot manipulator (MRM) include performance optimization and solving the coupling effect between the human and the robot as well as MRM subsystems. In this paper, a cooperative game-based decentralized optimal control approach is developed for MRMs with pHRI. The joint torque feedback (JTF) technique is utilized to form the MRM dynamic model, then, the major objective of optimal control with pHRI is transformed into approximating Pareto equilibrium by adopting cooperative game governed between the human and the MRM that are regarded as players with different tasks and optimization criteria in interaction process. On the basis of adaptive dynamic programming (ADP) algorithm, the decentralized approximate optimal control strategy with pHRI is developed by a critic neural network (NN)-based cooperative game manner for solving the coupled Hamilton–Jacobian–Bellman (HJB) equation. The position tracking error under pHRI is verified to be ultimately uniformly bounded (UUB). Experiment results have been presented, which exhibit the superiority of the proposed method. [ABSTRACT FROM AUTHOR]