Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems

doi:10.1109/TETCI.2023.3301789

	Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems
	Zhang, Yong 1; Mu, Chaoxu 1; Zhao, Dongbin2,3,4
发表期刊	IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
ISSN	2471-285X
	2023-09-04
页码	12
通讯作者	Mu, Chaoxu(cxmu@tju.edu.cn)
摘要	Reinforcement learning (RL) algorithms require continuous interaction with the controlled plant to optimize the objective function and control policy. However, the security risk and poor real-time performance limit the application of RL algorithms. In this article, a data generation feedback relearning (DGFR) control algorithm is developed to avoid these limitations. The DGFR control algorithm improves the control performance by interacting with a proposed delayed neural network-based data generation model. Considering the time-delay characteristics of industrial production, the data generation model is different from the identifier of the system model, but it is used to generate the target data required by the DGFR control algorithm. The interaction between the algorithm and the data generation model avoids the risk of trial-and-error and improves real-time performance. Finally, the superiority of the proposed control algorithm is verified by giving comparative experiments in conjunction with a power grid system and a permanent magnet synchronous motor system.
关键词	Data models Real-time systems Heuristic algorithms Mathematical models Adaptation models Approximation algorithms Cost function Data generation model feedback relearning control delayed neural network reinforcement learning unmodeled nonlinear system
DOI	10.1109/TETCI.2023.3301789
关键词[WOS]	H-INFINITY CONTROL ; ZERO-SUM GAMES ; STABILITY ; ALGORITHM ; STATE
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021YFB1714700] ; National Natural Science Foundation of China[62022061]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001064590300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53189
专题	多模态人工智能系统全国重点实验室
通讯作者	Mu, Chaoxu
作者单位	1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, 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.Peng Cheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Yong,Mu, Chaoxu,Zhao, Dongbin. Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems[J]. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,2023:12.
APA	Zhang, Yong,Mu, Chaoxu,&Zhao, Dongbin.(2023).Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems.IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,12.
MLA	Zhang, Yong,et al."Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems".IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2023):12.