{"title":"Continuous-time decentralized wavelet neural control for a 2 DOF robot manipulator","authors":"Luis A. Vázquez, F. Jurado","doi":"10.1109/ICEEE.2014.6978295","DOIUrl":null,"url":null,"abstract":"This paper presents a decentralized wavelet neural control scheme for trajectory tracking of a two degrees of freedom (DOF) vertical robot manipulator. A decentralized recurrent wavelet first order neural network (RWFONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error (FE) training algorithm, the dynamics behavior of the plant. Based on the RWFONN subsystem, a local neural controller is designed via backstepping approach. The performance of the decentralized wavelet neural controller is validated via simulation.","PeriodicalId":6661,"journal":{"name":"2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","volume":"12 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEE.2014.6978295","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper presents a decentralized wavelet neural control scheme for trajectory tracking of a two degrees of freedom (DOF) vertical robot manipulator. A decentralized recurrent wavelet first order neural network (RWFONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error (FE) training algorithm, the dynamics behavior of the plant. Based on the RWFONN subsystem, a local neural controller is designed via backstepping approach. The performance of the decentralized wavelet neural controller is validated via simulation.
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