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Summary: This article presents a multiplayer zero‐sum game optimal control for modular robot manipulators (MRMs) via adaptive dynamic programming (ADP). The dynamic model of MRMs with interconnected dynamic couplings (IDCs) is formulated. The control law and IDC effect are regarded as players in each module. The optimal tracking control problem of the MRM system can be transformed into a multiplayer zero‐sum game. Base on ADP algorithm, the Hamilton–Jacobi–Issacs (HJI) equation is solved via critic neural network. Then, the optimal control law can be derivated. The closed‐loop robotic system is proved to be asymptotic stable and experiments are conducted to verify the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy logic nonzero-sum game-based distributed approximated optimal control scheme is presented for modular robot manipulators (MRMs) with human-robot collaboration (HRC) tasks. The MRM dynamic model is formulated by using joint torque feedback (JTF) technique. Based on the differential game strategy, the optimal control problem of HRC task-oriented MRM systems is transformed into a nonzero-sum game problem of multiple robotic subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, the distributed approximate optimal control policy under HRC tasks is developed by a novel fuzzy logic nonzero-sum game manner for solving the coupled Hamilton–Jacobian (HJ) equation. The trajectory tracking error under HRC task of the closed-loop MRM system is proved to be ultimately uniformly bounded (UUB) using the Lyapunov theory. Finally, experiment results have been presented, which demonstrate the advantage and effectiveness of the developed method. [ABSTRACT FROM AUTHOR]