{"title":"基于RBFNN估计的无模型自适应超扭滑模速度控制","authors":"Qingfang Teng, Xiaojian Wang, Kai Xu","doi":"10.1049/elp2.70048","DOIUrl":null,"url":null,"abstract":"<p>Considering the various unknown and uncertain parameters as well as load disturbances of permanent magnet linear synchronous motor (PMLSM) drive systems, this paper proposes a novel model-free adaptive super-twisting (MFAST) speed control strategy based on radial basis function neural network (RBFNN) estimator to ensure the satisfactory performance and strong robustness of the speed control. First, by considering all possible unknown and uncertain parameters, the ultralocal model of PMLSM is constructed. Next, the RBFNN estimator is designed to estimate the unknown parameters of the above-mentioned ultralocal model. Finally, the RBFNN-based MFAST control law is proposed to guarantee PMLSM drive systems' robustness against various internal and external disturbances. StarSim HIL experiment results demonstrate that the synthesised RBFNN-based MFAST control strategy can enable PMLSM drive systems to possess high accuracy, remarkable rapidity and strong robustness.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"19 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70048","citationCount":"0","resultStr":"{\"title\":\"Model-Free Adaptive Super-Twisting Sliding Mode Speed Control Based on RBFNN Estimator for PMLSM Drive Systems\",\"authors\":\"Qingfang Teng, Xiaojian Wang, Kai Xu\",\"doi\":\"10.1049/elp2.70048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Considering the various unknown and uncertain parameters as well as load disturbances of permanent magnet linear synchronous motor (PMLSM) drive systems, this paper proposes a novel model-free adaptive super-twisting (MFAST) speed control strategy based on radial basis function neural network (RBFNN) estimator to ensure the satisfactory performance and strong robustness of the speed control. First, by considering all possible unknown and uncertain parameters, the ultralocal model of PMLSM is constructed. Next, the RBFNN estimator is designed to estimate the unknown parameters of the above-mentioned ultralocal model. Finally, the RBFNN-based MFAST control law is proposed to guarantee PMLSM drive systems' robustness against various internal and external disturbances. StarSim HIL experiment results demonstrate that the synthesised RBFNN-based MFAST control strategy can enable PMLSM drive systems to possess high accuracy, remarkable rapidity and strong robustness.</p>\",\"PeriodicalId\":13352,\"journal\":{\"name\":\"Iet Electric Power Applications\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2025-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70048\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Iet Electric Power Applications\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/elp2.70048\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iet Electric Power Applications","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/elp2.70048","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Model-Free Adaptive Super-Twisting Sliding Mode Speed Control Based on RBFNN Estimator for PMLSM Drive Systems
Considering the various unknown and uncertain parameters as well as load disturbances of permanent magnet linear synchronous motor (PMLSM) drive systems, this paper proposes a novel model-free adaptive super-twisting (MFAST) speed control strategy based on radial basis function neural network (RBFNN) estimator to ensure the satisfactory performance and strong robustness of the speed control. First, by considering all possible unknown and uncertain parameters, the ultralocal model of PMLSM is constructed. Next, the RBFNN estimator is designed to estimate the unknown parameters of the above-mentioned ultralocal model. Finally, the RBFNN-based MFAST control law is proposed to guarantee PMLSM drive systems' robustness against various internal and external disturbances. StarSim HIL experiment results demonstrate that the synthesised RBFNN-based MFAST control strategy can enable PMLSM drive systems to possess high accuracy, remarkable rapidity and strong robustness.
期刊介绍：
IET Electric Power Applications publishes papers of a high technical standard with a suitable balance of practice and theory. The scope covers a wide range of applications and apparatus in the power field. In addition to papers focussing on the design and development of electrical equipment, papers relying on analysis are also sought, provided that the arguments are conveyed succinctly and the conclusions are clear.
The scope of the journal includes the following:
The design and analysis of motors and generators of all sizes
Rotating electrical machines
Linear machines
Actuators
Power transformers
Railway traction machines and drives
Variable speed drives
Machines and drives for electrically powered vehicles
Industrial and non-industrial applications and processes
Current Special Issue. Call for papers:
Progress in Electric Machines, Power Converters and their Control for Wave Energy Generation - https://digital-library.theiet.org/files/IET_EPA_CFP_PEMPCCWEG.pdf
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