{"title":"An adaptive impedance control method for blade polishing based on the Kalman filter","authors":"","doi":"10.1007/s00170-024-13401-5","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Robotic force control is crucial for precise polishing and has a significant influence on the final effects. The blade has a free-form surface in space, and the curvature changes drastically, making traditional impedance control feedback untimely. To solve this problem, this paper proposes an adaptive impedance control method for blade polishing based on Kalman filter. The force data is denoised by Kalman filtering to obtain the real force data, then the data is gravity compensated to obtain the real polishing force. The method analyzes the influences of stiffness change and displacement change on the polishing force, and establishes a stiffness and displacement coupling compensation model. The method achieves timely feedback when the robot copes with unknown environmental stiffness changes. In addition, the Lyapunov function is applied to verify the stability of the method during implementation. Four processing conditions are simulated by using Matlab Simulink. The results indicate that the proposed method can provide faster response and higher force tracking accuracy by adjusting the reference position when the environment changes. In the experiment of polishing blade, the roughness is reduced to below Ra0.32 μm and fluctuation range of polishing force is within ±1 N. The force control method performance is significantly improved and the blade surface quality is effectively improved.</p>","PeriodicalId":50345,"journal":{"name":"International Journal of Advanced Manufacturing Technology","volume":"31 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advanced Manufacturing Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00170-024-13401-5","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Robotic force control is crucial for precise polishing and has a significant influence on the final effects. The blade has a free-form surface in space, and the curvature changes drastically, making traditional impedance control feedback untimely. To solve this problem, this paper proposes an adaptive impedance control method for blade polishing based on Kalman filter. The force data is denoised by Kalman filtering to obtain the real force data, then the data is gravity compensated to obtain the real polishing force. The method analyzes the influences of stiffness change and displacement change on the polishing force, and establishes a stiffness and displacement coupling compensation model. The method achieves timely feedback when the robot copes with unknown environmental stiffness changes. In addition, the Lyapunov function is applied to verify the stability of the method during implementation. Four processing conditions are simulated by using Matlab Simulink. The results indicate that the proposed method can provide faster response and higher force tracking accuracy by adjusting the reference position when the environment changes. In the experiment of polishing blade, the roughness is reduced to below Ra0.32 μm and fluctuation range of polishing force is within ±1 N. The force control method performance is significantly improved and the blade surface quality is effectively improved.
期刊介绍：
The International Journal of Advanced Manufacturing Technology bridges the gap between pure research journals and the more practical publications on advanced manufacturing and systems. It therefore provides an outstanding forum for papers covering applications-based research topics relevant to manufacturing processes, machines and process integration.
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