Fuzzy logic nonzero-sum game-based distributed approximated optimal control of modular robot manipulators with human-robot collaboration

Abstract

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.