Adaptive iterative learning control based on particle swarm optimization | |
Gu, Qun2; Hao, Xiaohong1 | |
刊名 | Journal of Supercomputing
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2020-05-01 | |
卷号 | 76期号:5页码:3615-3622 |
关键词 | Adaptive algorithms Iterative methods Optimization Particle swarm optimization (PSO) Swarm intelligence Two term control systems Adaptive iterative learning control Adaptive optimization Constraints Convergence Convergence rates Iterative learning control Iterative learning control algorithm Particle swarm |
ISSN号 | 09208542 |
DOI | 10.1007/s11227-018-2566-4 |
英文摘要 | The convergence rate of the traditional iterative learning control algorithm is slow, and the application range is narrow. This paper mainly focuses on the optimization of iterative learning control algorithm. It improves the traditional iterative learning control algorithm, and improves the iterative learning control algorithm through particle swarm adaptive algorithm. An adaptive optimization iterative learning control algorithm with particle swarm is proposed. Not only can the convergence speed of the algorithm be improved, but also the uncertainty of the model in the algorithm is solved. © 2018, Springer Science+Business Media, LLC, part of Springer Nature. |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | Springer |
WOS记录号 | WOS:000531462600034 |
内容类型 | 期刊论文 |
源URL | [http://ir.lut.edu.cn/handle/2XXMBERH/115081] ![]() |
专题 | 计算机与通信学院 电气工程与信息工程学院 |
作者单位 | 1.School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China 2.School of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China; |
推荐引用方式 GB/T 7714 | Gu, Qun,Hao, Xiaohong. Adaptive iterative learning control based on particle swarm optimization[J]. Journal of Supercomputing,2020,76(5):3615-3622. |
APA | Gu, Qun,&Hao, Xiaohong.(2020).Adaptive iterative learning control based on particle swarm optimization.Journal of Supercomputing,76(5),3615-3622. |
MLA | Gu, Qun,et al."Adaptive iterative learning control based on particle swarm optimization".Journal of Supercomputing 76.5(2020):3615-3622. |
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