{"title":"X-Y工作台位置控制的自适应模糊迭代学习控制器","authors":"O. Elshazly, M. El-Bardini, N. El-Rabaie","doi":"10.1109/AQTR.2014.6857832","DOIUrl":null,"url":null,"abstract":"In this paper, an adaptive fuzzy iterative learning control algorithm is proposed for controlling one of the Mecha-tronics systems. The proposed control scheme is based upon a proportional-derivative-integral (PID) iterative learning control (ILC), for which a fuzzy control is added to tune the parameters of the PID-type ILC. Moreover, an adaptation law is added to the fuzzy control in order to automatically select the proper fuzzy membership functions. The performance of proposed algorithm was assessed in computer numerical controlled (CNC) machine X-Y table to illustrate the validation and the effectiveness of the proposed procedure. The simulation results show that the proposed algorithm can reduce the trajectory error in a far less number of iterations.","PeriodicalId":297141,"journal":{"name":"2014 IEEE International Conference on Automation, Quality and Testing, Robotics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Adaptive fuzzy iterative learning controller for X-Y table position control\",\"authors\":\"O. Elshazly, M. El-Bardini, N. El-Rabaie\",\"doi\":\"10.1109/AQTR.2014.6857832\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an adaptive fuzzy iterative learning control algorithm is proposed for controlling one of the Mecha-tronics systems. The proposed control scheme is based upon a proportional-derivative-integral (PID) iterative learning control (ILC), for which a fuzzy control is added to tune the parameters of the PID-type ILC. Moreover, an adaptation law is added to the fuzzy control in order to automatically select the proper fuzzy membership functions. The performance of proposed algorithm was assessed in computer numerical controlled (CNC) machine X-Y table to illustrate the validation and the effectiveness of the proposed procedure. The simulation results show that the proposed algorithm can reduce the trajectory error in a far less number of iterations.\",\"PeriodicalId\":297141,\"journal\":{\"name\":\"2014 IEEE International Conference on Automation, Quality and Testing, Robotics\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Automation, Quality and Testing, Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AQTR.2014.6857832\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Automation, Quality and Testing, Robotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AQTR.2014.6857832","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive fuzzy iterative learning controller for X-Y table position control
In this paper, an adaptive fuzzy iterative learning control algorithm is proposed for controlling one of the Mecha-tronics systems. The proposed control scheme is based upon a proportional-derivative-integral (PID) iterative learning control (ILC), for which a fuzzy control is added to tune the parameters of the PID-type ILC. Moreover, an adaptation law is added to the fuzzy control in order to automatically select the proper fuzzy membership functions. The performance of proposed algorithm was assessed in computer numerical controlled (CNC) machine X-Y table to illustrate the validation and the effectiveness of the proposed procedure. The simulation results show that the proposed algorithm can reduce the trajectory error in a far less number of iterations.