Learning for Depth Control of a Robotic Penguin: A Data-Driven Model Predictive Control Approach

doi:10.1109/TIE.2022.3225840

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	Learning for Depth Control of a Robotic Penguin: A Data-Driven Model Predictive Control Approach
	Pan, Jie 1; Zhang, Pengfei 2,3; Wang, Jian 3; Liu, Mingxin 4; Yu, Junzhi 1
刊名	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
	2023-11-01
卷号	70 期号:11 页码:11422-11432
关键词	Data-driven model predictive control (MPC) depth control motion control reinforcement learning (RL) robotic penguin
ISSN号	0278-0046
DOI	10.1109/TIE.2022.3225840
通讯作者	Yu, Junzhi(junzhi.yu@ia.ac.cn)
英文摘要	For bionic underwater robots, it is a great challenge for depth control due to model uncertainty and strong nonlinearity. To this end, we propose a data-driven model predictive control (MPC) approach using reinforcement learning (RL) for robotic penguin depth control. First, by imitating the underwater mode of the biological penguin, a robotic prototype with a tendon-driven head, two-degrees-of-freedom wings, and a tendon-driven tail was designed. Then, a data-driven MPC framework is proposed considering the structure and motion properties of the robotic penguin. Especially, a data-based learning environment is constructed using a motion capture system, computational fluid dynamics, and a backpropagation neural network. Meanwhile, to maximize the benefits of the controller while ensuring safety and stability, a data-driven MPC using the RL scheme is applied to approximate the optimal policy. Combined with an appropriate reward design and periodic training, the closed-loop controller performance is significantly improved, and the validity of the proposed framework is finally tested by extensive simulations and experiments. Notably, this work will provide valuable insights into the learning-based motion control of bionic underwater robots.
资助项目	National Natural Science Foundation of China[62233001] ; National Natural Science Foundation of China[U1909206] ; National Natural Science Foundation of China[62073196] ; National Natural Science Foundation of China[T2121002] ; Joint Fund of Ministry of Education for Equipment PreResearch[8091B022134] ; S&T Program of Hebei[F2020203037]
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:000986714600060
资助机构	National Natural Science Foundation of China ; Joint Fund of Ministry of Education for Equipment PreResearch ; S&T Program of Hebei
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53447]
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.Peking Univ, Dept Adv Mfg & Robot, Coll Engn, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China
推荐引用方式 GB/T 7714	Pan, Jie,Zhang, Pengfei,Wang, Jian,et al. Learning for Depth Control of a Robotic Penguin: A Data-Driven Model Predictive Control Approach[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2023,70(11):11422-11432.
APA	Pan, Jie,Zhang, Pengfei,Wang, Jian,Liu, Mingxin,&Yu, Junzhi.(2023).Learning for Depth Control of a Robotic Penguin: A Data-Driven Model Predictive Control Approach.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,70(11),11422-11432.
MLA	Pan, Jie,et al."Learning for Depth Control of a Robotic Penguin: A Data-Driven Model Predictive Control Approach".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 70.11(2023):11422-11432.