{"title":"Online identification and feed-forward compensation of nonlinear friction in servo system based on RBF neural network model","authors":"Yuheng Zhu, Xuewei Li, Lingyi Kong, Taihao Zhang, Guangming Zheng","doi":"10.1139/tcsme-2023-0168","DOIUrl":null,"url":null,"abstract":"In this paper, an online identification and compensation method of nonlinear friction based on radial basis function (RBF) neural network model is proposed for the influence of nonlinear friction on machining accuracy in the low speed process of servo feed system of CNC machine tools. First, a three-layer single-input-output RBF neural network model is established for describing the nonlinear friction of servo feeding system. Second, the neural network online learning algorithm is improved based on adaptive gain, which improves the stability and accuracy of the algorithm. Finally, experiments were carried out on a three-axis milling machine to compensate the friction in the servo feed system in real time based on the online identification results. The results show that the method can effectively improve the online identification accuracy and convergence rate, and effectively improved the low-speed performance of the servo feed system.","PeriodicalId":23285,"journal":{"name":"Transactions of The Canadian Society for Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of The Canadian Society for Mechanical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1139/tcsme-2023-0168","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, an online identification and compensation method of nonlinear friction based on radial basis function (RBF) neural network model is proposed for the influence of nonlinear friction on machining accuracy in the low speed process of servo feed system of CNC machine tools. First, a three-layer single-input-output RBF neural network model is established for describing the nonlinear friction of servo feeding system. Second, the neural network online learning algorithm is improved based on adaptive gain, which improves the stability and accuracy of the algorithm. Finally, experiments were carried out on a three-axis milling machine to compensate the friction in the servo feed system in real time based on the online identification results. The results show that the method can effectively improve the online identification accuracy and convergence rate, and effectively improved the low-speed performance of the servo feed system.
期刊介绍:
Published since 1972, Transactions of the Canadian Society for Mechanical Engineering is a quarterly journal that publishes comprehensive research articles and notes in the broad field of mechanical engineering. New advances in energy systems, biomechanics, engineering analysis and design, environmental engineering, materials technology, advanced manufacturing, mechatronics, MEMS, nanotechnology, thermo-fluids engineering, and transportation systems are featured.