Data-driven Multiplayer Mixed-zero-sum Game Control of Modular Robot Manipulators with Uncertain Disturbance

This paper develops a data-driven multiplayer mixed-zero-sum game control approach of modular robot manipulators (MRMs) with uncertain disturbance via adaptive dynamic programming (ADP). The dynamic model of MRMs is formulated via joint torque feedback. We deem n modules and uncertain disturbance as...

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Bibliographic Details
Published in:International journal of control, automation, and systems automation, and systems, 2023-02, Vol.21 (2), p.645-657
Main Authors: Zhu, Xinye, An, Tianjiao, Dong, Bo
Format: Article
Language:English
Subjects:
Adaptive control
Closed loops
Control
Control theory
Couplings
Dynamic models
Dynamic programming
Engineering
Game theory
Manipulators
Mechatronics
Optimal control
Recurrent neural networks
Regular Papers
Robot arms
Robot control
Robotics
Zero sum games
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Summary:This paper develops a data-driven multiplayer mixed-zero-sum game control approach of modular robot manipulators (MRMs) with uncertain disturbance via adaptive dynamic programming (ADP). The dynamic model of MRMs is formulated via joint torque feedback. We deem n modules and uncertain disturbance as players in the game theory structure. The uncertainties in the MRM system such as joint friction and interconnected dynamic couplings (IDCs), we employ data-driven model based on recurrent neural networks (RNNs) to built. According to ADP, the Hamilton-Jacobi (HJ) equation can be solved by using critic neural networks and derivate the optimal control law. The closed-loop robotic system is proved to be asymptotic stable via mixed-zero-sum game control. Experiments are conducted to verify the effectiveness.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-021-1021-0