Reliable home error identification of a 2-DOF parallel robot based on regularization methods
This paper deals with the home error (the initial position error of active link) identification of a 2-DOF parallel robot. The identification model containing the home errors of the robot and the assembly errors of a new measuring instrument is developed using distance measurement. After that, an ad...
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| Veröffentlicht in: | Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science Journal of mechanical engineering science, 2019-04, Vol.233 (7), p.2502-2515 |
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| container_title | Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science |
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| creator | Mei, Jiangping Zang, Jiawei Ding, Yabin |
| description | This paper deals with the home error (the initial position error of active link) identification of a 2-DOF parallel robot. The identification model containing the home errors of the robot and the assembly errors of a new measuring instrument is developed using distance measurement. After that, an adaptive ridge regression method and a regularized Kalman filter method are proposed to improve the identification reliability and accuracy. Particularly, a modified L-curve method is proposed to provide suitable regularization parameters for the regularized Kalman filter. Based on the selected optimal measurement positions, experiments are carried out, in which the two regularized identification methods are compared with the ordinary ridge regression and the Kalman filter methods. Results show that the reliability and accuracy of the two methods are much better than the ordinary ridge regression method, and the divergence problem of the Kalman filter can be well resolved by the regularized Kalman filter. |
| doi_str_mv | 10.1177/0954406218791635 |
| format | Article |
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The identification model containing the home errors of the robot and the assembly errors of a new measuring instrument is developed using distance measurement. After that, an adaptive ridge regression method and a regularized Kalman filter method are proposed to improve the identification reliability and accuracy. Particularly, a modified L-curve method is proposed to provide suitable regularization parameters for the regularized Kalman filter. Based on the selected optimal measurement positions, experiments are carried out, in which the two regularized identification methods are compared with the ordinary ridge regression and the Kalman filter methods. 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Results show that the reliability and accuracy of the two methods are much better than the ordinary ridge regression method, and the divergence problem of the Kalman filter can be well resolved by the regularized Kalman filter.</description><subject>Adaptive filters</subject><subject>Distance measurement</subject><subject>Divergence</subject><subject>Identification</subject><subject>Identification methods</subject><subject>Kalman filters</subject><subject>Measuring instruments</subject><subject>Parallel degrees of freedom</subject><subject>Parameter modification</subject><subject>Position errors</subject><subject>Regression</subject><subject>Regularization</subject><subject>Regularization methods</subject><subject>Reliability</subject><subject>Robots</subject><issn>0954-4062</issn><issn>2041-2983</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kM1LAzEUxIMoWKt3jwHPq3n52s1RqlVBKIjehCWbvLRbtk1Ntgf9692ygiD4LnOY38yDIeQS2DVAWd4wo6RkmkNVGtBCHZEJZxIKbipxTCYHuzj4p-Qs5zUbjms1Ie8v2LW26ZCu4gYpphQTbT1u-za0zvZt3NIYqKW8uFvM6c4m23XY0RSb2NPGZvR0QBIu951N7deY2GC_ij6fk5Ngu4wXPzolb_P719lj8bx4eJrdPhdOMNMXBowtuRbeB62DkhwsU8KpIK3wrnFaWgelbxpmZKVBgeIKvEYpUXHHUUzJ1di7S_Fjj7mv13GftsPLmoMRwKWp2ECxkXIp5pww1LvUbmz6rIHVhw3rvxsOkWKMZLvE39J_-W_62XAi</recordid><startdate>201904</startdate><enddate>201904</enddate><creator>Mei, Jiangping</creator><creator>Zang, Jiawei</creator><creator>Ding, Yabin</creator><general>SAGE Publications</general><general>SAGE PUBLICATIONS, INC</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><orcidid>https://orcid.org/0000-0002-4247-1068</orcidid></search><sort><creationdate>201904</creationdate><title>Reliable home error identification of a 2-DOF parallel robot based on regularization methods</title><author>Mei, Jiangping ; Zang, Jiawei ; Ding, Yabin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c309t-919a7263ddf66f5421a053c5f4a3dcbc64ac17dbb094861515251d6e44e52c2e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adaptive filters</topic><topic>Distance measurement</topic><topic>Divergence</topic><topic>Identification</topic><topic>Identification methods</topic><topic>Kalman filters</topic><topic>Measuring instruments</topic><topic>Parallel degrees of freedom</topic><topic>Parameter modification</topic><topic>Position errors</topic><topic>Regression</topic><topic>Regularization</topic><topic>Regularization methods</topic><topic>Reliability</topic><topic>Robots</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mei, Jiangping</creatorcontrib><creatorcontrib>Zang, Jiawei</creatorcontrib><creatorcontrib>Ding, Yabin</creatorcontrib><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mei, Jiangping</au><au>Zang, Jiawei</au><au>Ding, Yabin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reliable home error identification of a 2-DOF parallel robot based on regularization methods</atitle><jtitle>Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science</jtitle><date>2019-04</date><risdate>2019</risdate><volume>233</volume><issue>7</issue><spage>2502</spage><epage>2515</epage><pages>2502-2515</pages><issn>0954-4062</issn><eissn>2041-2983</eissn><abstract>This paper deals with the home error (the initial position error of active link) identification of a 2-DOF parallel robot. The identification model containing the home errors of the robot and the assembly errors of a new measuring instrument is developed using distance measurement. After that, an adaptive ridge regression method and a regularized Kalman filter method are proposed to improve the identification reliability and accuracy. Particularly, a modified L-curve method is proposed to provide suitable regularization parameters for the regularized Kalman filter. Based on the selected optimal measurement positions, experiments are carried out, in which the two regularized identification methods are compared with the ordinary ridge regression and the Kalman filter methods. 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| ispartof | Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science, 2019-04, Vol.233 (7), p.2502-2515 |
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| subjects | Adaptive filters Distance measurement Divergence Identification Identification methods Kalman filters Measuring instruments Parallel degrees of freedom Parameter modification Position errors Regression Regularization Regularization methods Reliability Robots |
| title | Reliable home error identification of a 2-DOF parallel robot based on regularization methods |
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