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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Vydáno v:Nonlinear dynamics Ročník 112; číslo 9; s. 7145 - 7158
Hlavní autoři: An, Tianjiao, Zhu, Xinye, Ma, Bing, Zhu, Mingchao, Dong, Bo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.05.2024
Springer Nature B.V
Témata:
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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Shrnutí: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.
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-024-09437-7