{"title":"基于超扭转扩展状态观测器的机器人假体地面反作用力估计","authors":"Yongshan Huang, Hongxu Ma, Jin Zhang, Honglei An","doi":"10.1109/ICARM52023.2021.9536209","DOIUrl":null,"url":null,"abstract":"A ground reaction forces (GRFs) estimation method based on super-twisting extended state observer (STESO) for robotic prosthesis is proposed. The load cells and pressure sensors are not needed for the proposed method, and also the model of GRFs, hence it could adapt to different terrain environments. The GRFs estimate method uses a globally integral and super-twisting sliding model, which enables the observation error to converge to zero in finite time, and the GRFs estimator would not crash even if the initial estimation error is large. The stability and finite time convergence of the observer is rigorously proved and analyzed mathematically. The simulation results prove the feasibility and effectiveness of the proposed GRFs estimates method.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ground Reaction Force Estimation in Robotic Prosthesis using Super-twisting Extended State Observer\",\"authors\":\"Yongshan Huang, Hongxu Ma, Jin Zhang, Honglei An\",\"doi\":\"10.1109/ICARM52023.2021.9536209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A ground reaction forces (GRFs) estimation method based on super-twisting extended state observer (STESO) for robotic prosthesis is proposed. The load cells and pressure sensors are not needed for the proposed method, and also the model of GRFs, hence it could adapt to different terrain environments. The GRFs estimate method uses a globally integral and super-twisting sliding model, which enables the observation error to converge to zero in finite time, and the GRFs estimator would not crash even if the initial estimation error is large. The stability and finite time convergence of the observer is rigorously proved and analyzed mathematically. The simulation results prove the feasibility and effectiveness of the proposed GRFs estimates method.\",\"PeriodicalId\":367307,\"journal\":{\"name\":\"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICARM52023.2021.9536209\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARM52023.2021.9536209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ground Reaction Force Estimation in Robotic Prosthesis using Super-twisting Extended State Observer
A ground reaction forces (GRFs) estimation method based on super-twisting extended state observer (STESO) for robotic prosthesis is proposed. The load cells and pressure sensors are not needed for the proposed method, and also the model of GRFs, hence it could adapt to different terrain environments. The GRFs estimate method uses a globally integral and super-twisting sliding model, which enables the observation error to converge to zero in finite time, and the GRFs estimator would not crash even if the initial estimation error is large. The stability and finite time convergence of the observer is rigorously proved and analyzed mathematically. The simulation results prove the feasibility and effectiveness of the proposed GRFs estimates method.