{"title":"时滞系统的改进自适应Smith预测控制方案","authors":"D. Feng, Feng Pan, Ru-cheng Han","doi":"10.1109/ICMLC.2002.1176797","DOIUrl":null,"url":null,"abstract":"This paper presents an improved self-adaptive Smith predictive control scheme for time-delay systems. An FNNC (Fuzzy Neural Network Controller) is used to control the plant instead of the conventional PID controller, an improved identification algorithm with an adaptive vector forgetting factor is used to identify time-varying parameters, and the Smith predictor is realised. Simulation results show that this method can suppress the effects of parameter variations.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"9 1","pages":"463-466 vol.1"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Improved self-adaptive Smith predictive control scheme for time-delay system\",\"authors\":\"D. Feng, Feng Pan, Ru-cheng Han\",\"doi\":\"10.1109/ICMLC.2002.1176797\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an improved self-adaptive Smith predictive control scheme for time-delay systems. An FNNC (Fuzzy Neural Network Controller) is used to control the plant instead of the conventional PID controller, an improved identification algorithm with an adaptive vector forgetting factor is used to identify time-varying parameters, and the Smith predictor is realised. Simulation results show that this method can suppress the effects of parameter variations.\",\"PeriodicalId\":90702,\"journal\":{\"name\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"volume\":\"9 1\",\"pages\":\"463-466 vol.1\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2002.1176797\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1176797","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved self-adaptive Smith predictive control scheme for time-delay system
This paper presents an improved self-adaptive Smith predictive control scheme for time-delay systems. An FNNC (Fuzzy Neural Network Controller) is used to control the plant instead of the conventional PID controller, an improved identification algorithm with an adaptive vector forgetting factor is used to identify time-varying parameters, and the Smith predictor is realised. Simulation results show that this method can suppress the effects of parameter variations.
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