I. Hassanzadeh, S. Khanmohammadi, J. Jiang, G. Alizadeh
{"title":"Implementation of a functional link net-ANFIS controller for a robot manipulator","authors":"I. Hassanzadeh, S. Khanmohammadi, J. Jiang, G. Alizadeh","doi":"10.1109/ROMOCO.2002.1177139","DOIUrl":null,"url":null,"abstract":"In this paper, a new hybrid network; FLN-ANFIS (functional link net-adaptive neuro fuzzy system), is proposed and used as a controller for robot path tracking purposes. The advantages of the ANFIS controller along with fast learning rate of FLN are combined to propose a new methodology for controlling robot arm movement. Furthermore, a classic PID controller as well as the intelligent controller such as NN, fuzzy and ANFIS are developed and their performances are compared both by simulation and implementation. A 5-bar linked robot is chosen as a test system for implementation. Simulation and implementation of different controllers under various conditions are carried out on this robot. Results show that the designed FLN-ANFIS network for robot control can provide very good performances through a wide range of operations by improving dynamic operations of the system.","PeriodicalId":213750,"journal":{"name":"Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02.","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROMOCO.2002.1177139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
In this paper, a new hybrid network; FLN-ANFIS (functional link net-adaptive neuro fuzzy system), is proposed and used as a controller for robot path tracking purposes. The advantages of the ANFIS controller along with fast learning rate of FLN are combined to propose a new methodology for controlling robot arm movement. Furthermore, a classic PID controller as well as the intelligent controller such as NN, fuzzy and ANFIS are developed and their performances are compared both by simulation and implementation. A 5-bar linked robot is chosen as a test system for implementation. Simulation and implementation of different controllers under various conditions are carried out on this robot. Results show that the designed FLN-ANFIS network for robot control can provide very good performances through a wide range of operations by improving dynamic operations of the system.
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