{"title":"A nonlinear control system using a fuzzy self tuning Grey predictor based on a PID controller","authors":"A. K. Kwan, D. Van Quang, Y. Il","doi":"10.1109/ICCAS.2010.5669782","DOIUrl":null,"url":null,"abstract":"In this paper, a nonlinear control system using a fuzzy self tuning Grey predictor based on a PID controller is proposed. Firstly, the PID controller is designed according to the Zieger-Nichos 2 method with fast response and high robustness. Secondly, the grey predictor is suggested to use to estimate the system response in a near future in order to improve the control performance. In addition, the step of Grey predictor is adjusted by using the fuzzy control to satisfy the control requirement. Consequently, the control system fastens rising time, shortens settling time, reduces steady state error to zero, oppresses overshoot of transient response, and also prevents disturbance. A detailed specification of the control structure and its design process as well as simulation results achieved from Matlab/Simulink program are also presented. The simulation results show that the proposed control method has the ability to apply for nonlinear systems with higher control performance.","PeriodicalId":158687,"journal":{"name":"ICCAS 2010","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICCAS 2010","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAS.2010.5669782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, a nonlinear control system using a fuzzy self tuning Grey predictor based on a PID controller is proposed. Firstly, the PID controller is designed according to the Zieger-Nichos 2 method with fast response and high robustness. Secondly, the grey predictor is suggested to use to estimate the system response in a near future in order to improve the control performance. In addition, the step of Grey predictor is adjusted by using the fuzzy control to satisfy the control requirement. Consequently, the control system fastens rising time, shortens settling time, reduces steady state error to zero, oppresses overshoot of transient response, and also prevents disturbance. A detailed specification of the control structure and its design process as well as simulation results achieved from Matlab/Simulink program are also presented. The simulation results show that the proposed control method has the ability to apply for nonlinear systems with higher control performance.
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