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Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems

文献类型：期刊论文

作者Zhang, Yong2; Mu, Chaoxu2; Zhao, Dongbin1,3,4
刊名IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
出版日期2023-09-04
页码12
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
ISSN号2471-285X
DOI10.1109/TETCI.2023.3301789
通讯作者Mu, Chaoxu(cxmu@tju.edu.cn)
英文摘要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.
WOS关键词H-INFINITY CONTROL ; ZERO-SUM GAMES ; STABILITY ; ALGORITHM ; STATE
资助项目National Key Research and Development Program of China[2021YFB1714700] ; National Natural Science Foundation of China[62022061]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001064590300001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/53188]  
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.Peng Cheng Lab, Shenzhen 518055, Peoples R China
2.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, 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.

入库方式： OAI收割

来源：自动化研究所

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