期刊
IEEE-ASME TRANSACTIONS ON MECHATRONICS
卷 27, 期 1, 页码 149-158出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2021.3059441
关键词
Manipulators; Neural networks; Manipulator dynamics; Mechatronics; Kinematics; IEEE transactions; Task analysis; Communication overhead; distributed control; game theory; neural networks; redundancy resolution
类别
资金
- National Key Research and Development Program of China [2017YFE0118900]
- Huawei Mindspore Academic Award Fund of Chinese Association of Artificial Intelligence [CAAIXSJLJJ-2020-009 A]
- Team Project of Natural Science Foundation of Qinghai Province, China [2020-ZJ-903]
- Key Laboratory of IoT of Qinghai [2020-ZJ-Y16]
- Natural Science Foundation of Gansu Province, China [20JR10RA639]
- Natural Science Foundation of Chongqing (China) [cstc2020jcyj-zdxmX0028]
- Research and Development Foundation of Nanchong (China) [20YFZJ0018]
- CAS Light of West China Program
- Chongqing Key Laboratory of Mobile Communications Technology [cqupt-mct-202004]
- Fundamental Research Funds for the Central Universities [lzujbky-2019-89, lzuxxxy-2019-tm20]
In this article, a multimanipulator cooperative control scheme with improved communication efficiency is proposed. The scheme formulates the entire control process from the perspective of game theory and uses a neural network solver to update the strategies of manipulators. Theoretical analysis and simulation results support the superiority of the proposed control strategy.
An efficiency-oriented solution is theoretically a preferred choice to support the efficient operation of a system. Although some studies on the multimanipulator system share the load of the control center by transforming the network topology, the whole system often suffers an increased communication burden. In this article, a multimanipulator cooperative control scheme with an improved communication efficiency is proposed to allocate limited communication resources reasonably. The entire control process is formulated from the perspective of the game theory, and finally, evolved into a problem of finding a Nash equilibrium with time-varying parameters. Then, a neural network solver is designed to update the strategies of manipulators. Theoretical analysis supports the convergence and robustness of the solver. In addition, Zeno behavior does not occur under the domination of the control strategy. Finally, simulative results reveal that the proposed control strategy has advantages over the traditional periodic control in communication.
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