{"title":"基于模糊逻辑的单连杆柔性机械臂改进入侵杂草优化控制器","authors":"H. Kasdirin, M. Assemgul, M. Tokhi","doi":"10.1109/EAIS.2015.7368800","DOIUrl":null,"url":null,"abstract":"This paper presents development of a fuzzy logic control (FLC) system with bio-inspired optimization algorithm for reference tracking control of a single-link flexible manipulator. In the proposed controller, a modified invasive weed optimization (MIWO) algorithm is employed to optimize the tuning parameters of fuzzy logic controller. Invasive weed optimization (IWO) is a bio-inspired search algorithm that mimics how weeds colonize a certain area in nature. Although the IWO algorithm is good at exploring a certain search space, it is found less effective in getting accurate results. The algorithm is modified by applying local knowledge in the standard deviation of its reproduction of offspring in each generation to narrow the accuracy and improve the local search ability. The MIWO algorithm is tested with five benchmark functions and the result are compared with those of the original invasive weed optimization algorithm. The original IWO and MIWO are used as parameter tuners for the developed FLC mechanism to evaluate the effectiveness of the modified algorithm. The performances of the tuned controllers based on MIWO and IWO are evaluated based on the hub-able output of the flexible manipulator system. The overall results show that the proposed MIWO algorithm gives better performance in comparison to its original predecessor algorithm.","PeriodicalId":325875,"journal":{"name":"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"1887 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Fuzzy logic based controller for a single-link flexible manipulator using modified invasive weed optimization\",\"authors\":\"H. Kasdirin, M. Assemgul, M. Tokhi\",\"doi\":\"10.1109/EAIS.2015.7368800\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents development of a fuzzy logic control (FLC) system with bio-inspired optimization algorithm for reference tracking control of a single-link flexible manipulator. In the proposed controller, a modified invasive weed optimization (MIWO) algorithm is employed to optimize the tuning parameters of fuzzy logic controller. Invasive weed optimization (IWO) is a bio-inspired search algorithm that mimics how weeds colonize a certain area in nature. Although the IWO algorithm is good at exploring a certain search space, it is found less effective in getting accurate results. The algorithm is modified by applying local knowledge in the standard deviation of its reproduction of offspring in each generation to narrow the accuracy and improve the local search ability. The MIWO algorithm is tested with five benchmark functions and the result are compared with those of the original invasive weed optimization algorithm. The original IWO and MIWO are used as parameter tuners for the developed FLC mechanism to evaluate the effectiveness of the modified algorithm. The performances of the tuned controllers based on MIWO and IWO are evaluated based on the hub-able output of the flexible manipulator system. The overall results show that the proposed MIWO algorithm gives better performance in comparison to its original predecessor algorithm.\",\"PeriodicalId\":325875,\"journal\":{\"name\":\"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)\",\"volume\":\"1887 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EAIS.2015.7368800\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EAIS.2015.7368800","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy logic based controller for a single-link flexible manipulator using modified invasive weed optimization
This paper presents development of a fuzzy logic control (FLC) system with bio-inspired optimization algorithm for reference tracking control of a single-link flexible manipulator. In the proposed controller, a modified invasive weed optimization (MIWO) algorithm is employed to optimize the tuning parameters of fuzzy logic controller. Invasive weed optimization (IWO) is a bio-inspired search algorithm that mimics how weeds colonize a certain area in nature. Although the IWO algorithm is good at exploring a certain search space, it is found less effective in getting accurate results. The algorithm is modified by applying local knowledge in the standard deviation of its reproduction of offspring in each generation to narrow the accuracy and improve the local search ability. The MIWO algorithm is tested with five benchmark functions and the result are compared with those of the original invasive weed optimization algorithm. The original IWO and MIWO are used as parameter tuners for the developed FLC mechanism to evaluate the effectiveness of the modified algorithm. The performances of the tuned controllers based on MIWO and IWO are evaluated based on the hub-able output of the flexible manipulator system. The overall results show that the proposed MIWO algorithm gives better performance in comparison to its original predecessor algorithm.