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

doi:10.1109/TIE.2022.3225840

	Learning for Depth Control of a Robotic Penguin: A Data-Driven Model Predictive Control Approach
	Pan, Jie 1; Zhang, Pengfei2,3 ; Wang, Jian3 ; Liu, Mingxin 4; Yu, Junzhi1
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
ISSN	0278-0046
	2023-11-01
卷号	70 期号:11 页码:11422-11432
通讯作者	Yu, Junzhi(junzhi.yu@ia.ac.cn)
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.
关键词	Data-driven model predictive control (MPC) depth control motion control reinforcement learning (RL) robotic penguin
DOI	10.1109/TIE.2022.3225840
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; Joint Fund of Ministry of Education for Equipment PreResearch ; S&T Program of Hebei
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000986714600060
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53448
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.