Cooperative Game-Based Approximate Optimal Control of Modular Robot Manipulators for Human-Robot Collaboration

Major challenges of controlling human-robot collaboration (HRC)-oriented modular robot manipulators (MRMs) include the estimation of human motion intention while cooperating with a robot and performance optimization. This article proposes a cooperative game-based approximate optimal control method o...

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Bibliographic Details
Published inIEEE transactions on cybernetics Vol. 53; no. 7; pp. 1 - 13
Main Authors An, Tianjiao, Wang, Yuexi, Liu, Guangjun, Li, Yuanchun, Dong, Bo
Format Journal Article
LanguageEnglish
Published United States IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary:Major challenges of controlling human-robot collaboration (HRC)-oriented modular robot manipulators (MRMs) include the estimation of human motion intention while cooperating with a robot and performance optimization. This article proposes a cooperative game-based approximate optimal control method of MRMs for HRC tasks. A harmonic drive compliance model-based human motion intention estimation method is developed using robot position measurements only, which forms the basis of the MRM dynamic model. Based on the cooperative differential game strategy, the optimal control problem of HRC-oriented MRM systems is transformed into a cooperative game problem of multiple subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a joint cost function identifier is developed via the critic neural networks, which is implemented for solving the parametric Hamilton-Jacobi-Bellman (HJB) equation and Pareto optimal solutions. The trajectory tracking error under the HRC task of the closed-loop MRM system is proved to be ultimately uniformly bounded (UUB) by the Lyapunov theory. Finally, experiment results are presented, which reveal the advantage of the proposed method.
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content type line 23
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2023.3277558