{"title":"A new iterative identification algorithm for estimating the LuGre friction model parameters","authors":"Saeed Mahmoudkhani , Johnathan Gorenstein , Keivan Ahmadi","doi":"10.1016/j.mechmachtheory.2024.105797","DOIUrl":null,"url":null,"abstract":"<div><div>The parameters of dynamic friction models like the LuGre model are commonly identified by computationally intensive nonlinear optimization methods. In this paper, an alternative least-square-based iterative algorithm is proposed to simultaneously identify the LuGre model parameters and the inertial properties from a limited set of measurements. The LuGre model, expressed in two forms allowed independent identification of the static and dynamic parameters. Moreover, since the method uses response-input time–history instead of numerous constant velocity experiments (CVEs), both the inertial and friction parameters can be identified in much fewer experiments (theoretically one). A variant of the Sparse Identification of Nonlinear Dynamics method called SR3 is embedded in the algorithm to capture the nonlinear viscous friction and Stribeck effects in the friction model. Application of the algorithm to an industrial robot joint shows that the convergence of the algorithm is fast and the identified model is accurate in predicting the joint’s dynamics in a wide range of velocities. The friction-velocity curves resulting from the identified model are compared to those obtained by traditional CVEs to confirm the accuracy of the identified model.</div></div>","PeriodicalId":49845,"journal":{"name":"Mechanism and Machine Theory","volume":"203 ","pages":"Article 105797"},"PeriodicalIF":4.5000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanism and Machine Theory","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0094114X24002246","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The parameters of dynamic friction models like the LuGre model are commonly identified by computationally intensive nonlinear optimization methods. In this paper, an alternative least-square-based iterative algorithm is proposed to simultaneously identify the LuGre model parameters and the inertial properties from a limited set of measurements. The LuGre model, expressed in two forms allowed independent identification of the static and dynamic parameters. Moreover, since the method uses response-input time–history instead of numerous constant velocity experiments (CVEs), both the inertial and friction parameters can be identified in much fewer experiments (theoretically one). A variant of the Sparse Identification of Nonlinear Dynamics method called SR3 is embedded in the algorithm to capture the nonlinear viscous friction and Stribeck effects in the friction model. Application of the algorithm to an industrial robot joint shows that the convergence of the algorithm is fast and the identified model is accurate in predicting the joint’s dynamics in a wide range of velocities. The friction-velocity curves resulting from the identified model are compared to those obtained by traditional CVEs to confirm the accuracy of the identified model.
期刊介绍:
Mechanism and Machine Theory provides a medium of communication between engineers and scientists engaged in research and development within the fields of knowledge embraced by IFToMM, the International Federation for the Promotion of Mechanism and Machine Science, therefore affiliated with IFToMM as its official research journal.
The main topics are:
Design Theory and Methodology;
Haptics and Human-Machine-Interfaces;
Robotics, Mechatronics and Micro-Machines;
Mechanisms, Mechanical Transmissions and Machines;
Kinematics, Dynamics, and Control of Mechanical Systems;
Applications to Bioengineering and Molecular Chemistry