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 c...

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Veröffentlicht in:	Nonlinear dynamics Jg. 112; H. 9; S. 7145 - 7158
Hauptverfasser:	An, Tianjiao, Zhu, Xinye, Ma, Bing, Zhu, Mingchao, Dong, Bo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.05.2024 Springer Nature B.V
Schlagworte:	Algorithms Approximation Automotive Engineering Classical Mechanics Control Cooperative control Dynamic models Dynamic programming Dynamical Systems Engineering Game theory Games Mechanical Engineering Neural networks Optimal control Optimization Original Paper Robot arms Robot control Robots Strategy Subsystems Tracking errors Vibration Physical human–robot interaction Adaptive dynamic programming Decentralized control Modular robot manipulators Cooperative game
ISSN:	0924-090X, 1573-269X
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Abstract	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.
AbstractList	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.
Author	Zhu, Xinye An, Tianjiao Zhu, Mingchao Dong, Bo Ma, Bing
Author_xml	– sequence: 1 givenname: Tianjiao orcidid: 0000-0002-4292-6691 surname: An fullname: An, Tianjiao organization: Department of Control Science and Engineering, Changchun University of Technology – sequence: 2 givenname: Xinye surname: Zhu fullname: Zhu, Xinye organization: Department of Control Science and Engineering, Changchun University of Technology – sequence: 3 givenname: Bing surname: Ma fullname: Ma, Bing organization: Department of Control Science and Engineering, Changchun University of Technology – sequence: 4 givenname: Mingchao surname: Zhu fullname: Zhu, Mingchao organization: CAS Key Laboratory of On-Orbit Manufacturing and Integration for Space Optics System, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences – sequence: 5 givenname: Bo orcidid: 0000-0003-3988-8054 surname: Dong fullname: Dong, Bo email: dongbo@ccut.edu.cn organization: Department of Control Science and Engineering, Changchun University of Technology
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SubjectTerms	Algorithms Approximation Automotive Engineering Classical Mechanics Control Cooperative control Dynamic models Dynamic programming Dynamical Systems Engineering Game theory Games Mechanical Engineering Neural networks Optimal control Optimization Original Paper Robot arms Robot control Robots Strategy Subsystems Tracking errors Vibration
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Title	Decentralized approximated optimal control for modular robot manipulations with physical human–robot interaction: a cooperative game-based strategy
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