Multiagent-Reinforcement-Learning-Based Stable Path Tracking Control for a Bionic Robotic Fish With Reaction Wheel

doi:10.1109/TIE.2023.3239937

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Multiagent-Reinforcement-Learning-Based Stable Path Tracking Control for a Bionic Robotic Fish With Reaction Wheel
	Qiu, Changlin 1,2; Wu, Zhengxing 1,2; Wang, Jian 1,2; Tan, Min 1,2; Yu, Junzhi 2,3,4
刊名	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
	2023-12-01
卷号	70 期号:12 页码:12670-12679
关键词	Multiagent reinforcement learning (MARL) path tracking control reaction wheel robotic fish underwater robot
ISSN号	0278-0046
DOI	10.1109/TIE.2023.3239937
通讯作者	Yu, Junzhi(junzhi.yu@ia.ac.cn)
英文摘要	The path tracking of the robotic fish is a hotspot with its high maneuverability and environmental friendliness. However, the periodic oscillation generated by bionic fish-like propulsion mode may lead to unstable control. To this end, this article proposes a novel framework involving a newly designed platform and multiagent reinforcement learning (MARL) method. First, a bionic robotic fish equipped with a reaction wheel is developed to enhance the stability. Second, an MARL-based control framework is proposed for the cooperative control of tail-beating and reaction wheel. Correspondingly, a hierarchical training method including initial training and iterative training is designed to deal with the control coupling and frequency difference between two agents. Finally, extensive simulations and experiments indicate that the developed robotic fish and the proposed MARL-based control framework can effectively improve the accuracy and stability of path tracking. Remarkably, headshaking is reduced about 40%. It provides a promising reference for the stability optimization and cooperative control of bionic swimming robots featuring oscillatory motions.
资助项目	National Natural Science Foundation of China[62233001] ; National Natural Science Foundation of China[62203436] ; National Natural Science Foundation of China[62022090] ; National Natural Science Foundation of China[62273351] ; National Natural Science Foundation of China[62203015] ; Ministry of Education for Equipment Pre-Research[8091B022134] ; Samp;T Program of Hebei[F2020203037]
WOS关键词	ATTITUDE
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001013415800076
资助机构	National Natural Science Foundation of China ; Ministry of Education for Equipment Pre-Research ; Samp;T Program of Hebei
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54110]
专题	多模态人工智能系统全国重点实验室
通讯作者	Yu, Junzhi
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China 4.Peking Univ, Coll Engn, Dept Adv Mfg & Robot, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China
推荐引用方式 GB/T 7714	Qiu, Changlin,Wu, Zhengxing,Wang, Jian,et al. Multiagent-Reinforcement-Learning-Based Stable Path Tracking Control for a Bionic Robotic Fish With Reaction Wheel[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2023,70(12):12670-12679.
APA	Qiu, Changlin,Wu, Zhengxing,Wang, Jian,Tan, Min,&Yu, Junzhi.(2023).Multiagent-Reinforcement-Learning-Based Stable Path Tracking Control for a Bionic Robotic Fish With Reaction Wheel.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,70(12),12670-12679.
MLA	Qiu, Changlin,et al."Multiagent-Reinforcement-Learning-Based Stable Path Tracking Control for a Bionic Robotic Fish With Reaction Wheel".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 70.12(2023):12670-12679.