{"title":"有源电力滤波器的自适应RBFNN模糊滑模控制","authors":"Tengteng Wang, J. Fei","doi":"10.1109/ICMA.2016.7558525","DOIUrl":null,"url":null,"abstract":"This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance performance of shunt active power filter (APF). The RBF NN is utilized on the approximation of nonlinear function in APF dynamic model, the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis, to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method confirm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.","PeriodicalId":260197,"journal":{"name":"2016 IEEE International Conference on Mechatronics and Automation","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Adaptive RBFNN fuzzy sliding mode control for active power filter\",\"authors\":\"Tengteng Wang, J. Fei\",\"doi\":\"10.1109/ICMA.2016.7558525\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance performance of shunt active power filter (APF). The RBF NN is utilized on the approximation of nonlinear function in APF dynamic model, the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis, to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method confirm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.\",\"PeriodicalId\":260197,\"journal\":{\"name\":\"2016 IEEE International Conference on Mechatronics and Automation\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Mechatronics and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMA.2016.7558525\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Mechatronics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMA.2016.7558525","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive RBFNN fuzzy sliding mode control for active power filter
This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance performance of shunt active power filter (APF). The RBF NN is utilized on the approximation of nonlinear function in APF dynamic model, the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis, to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method confirm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.