{"title":"数控机床进给系统 GMS 摩擦模型的有效参数识别","authors":"Dehai Huang, Jianzhong Yang, Guangda Xu, Huicheng Zhou, Jiakang Chen","doi":"10.1016/j.conengprac.2024.106061","DOIUrl":null,"url":null,"abstract":"<div><p>One of the main factors influencing machine tool feed system tracking performance is friction. By creating an accurate friction model and implementing feed-forward compensation based on the model, the negative impacts of friction can be efficiently reduced. The generalized Maxwell-slip (GMS) model is commonly used to model feed system friction; however, simple and effective parameter identification methods are lacking. In this paper, a parameter identification method based on a metaheuristic Gaussian swarm optimization (GSO) algorithm is proposed. The method divides the parameters into two parts via a theoretical derivation, and employs GSO to identify each part successively. The proposed GSO is a novel metaheuristic algorithm inspired by the Gaussian probability function. The excellent performance of the GSO ensures that the friction parameters can be accurately and quickly identified. The results of the simulation and physical identification experiments show that the proposed GSO-based identification method can accurately identify the parameters of the GMS model with average and maximum relative errors of 3.96% and 14.05%, respectively. The identified model can accurately predict the friction of the feed system. Additionally, after friction compensation, the tracking error was decreased by an average of 78.9%.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"152 ","pages":"Article 106061"},"PeriodicalIF":5.4000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effective parameter identification of the GMS friction model for feed systems in CNC machines\",\"authors\":\"Dehai Huang, Jianzhong Yang, Guangda Xu, Huicheng Zhou, Jiakang Chen\",\"doi\":\"10.1016/j.conengprac.2024.106061\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>One of the main factors influencing machine tool feed system tracking performance is friction. By creating an accurate friction model and implementing feed-forward compensation based on the model, the negative impacts of friction can be efficiently reduced. The generalized Maxwell-slip (GMS) model is commonly used to model feed system friction; however, simple and effective parameter identification methods are lacking. In this paper, a parameter identification method based on a metaheuristic Gaussian swarm optimization (GSO) algorithm is proposed. The method divides the parameters into two parts via a theoretical derivation, and employs GSO to identify each part successively. The proposed GSO is a novel metaheuristic algorithm inspired by the Gaussian probability function. The excellent performance of the GSO ensures that the friction parameters can be accurately and quickly identified. The results of the simulation and physical identification experiments show that the proposed GSO-based identification method can accurately identify the parameters of the GMS model with average and maximum relative errors of 3.96% and 14.05%, respectively. The identified model can accurately predict the friction of the feed system. Additionally, after friction compensation, the tracking error was decreased by an average of 78.9%.</p></div>\",\"PeriodicalId\":50615,\"journal\":{\"name\":\"Control Engineering Practice\",\"volume\":\"152 \",\"pages\":\"Article 106061\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Control Engineering Practice\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S096706612400220X\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S096706612400220X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Effective parameter identification of the GMS friction model for feed systems in CNC machines
One of the main factors influencing machine tool feed system tracking performance is friction. By creating an accurate friction model and implementing feed-forward compensation based on the model, the negative impacts of friction can be efficiently reduced. The generalized Maxwell-slip (GMS) model is commonly used to model feed system friction; however, simple and effective parameter identification methods are lacking. In this paper, a parameter identification method based on a metaheuristic Gaussian swarm optimization (GSO) algorithm is proposed. The method divides the parameters into two parts via a theoretical derivation, and employs GSO to identify each part successively. The proposed GSO is a novel metaheuristic algorithm inspired by the Gaussian probability function. The excellent performance of the GSO ensures that the friction parameters can be accurately and quickly identified. The results of the simulation and physical identification experiments show that the proposed GSO-based identification method can accurately identify the parameters of the GMS model with average and maximum relative errors of 3.96% and 14.05%, respectively. The identified model can accurately predict the friction of the feed system. Additionally, after friction compensation, the tracking error was decreased by an average of 78.9%.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.