{"title":"基于自适应神经网络的刚柔混合关节机器人虚拟分解控制","authors":"Wei Xia, Huashan Liu","doi":"10.1109/WRCSARA53879.2021.9612616","DOIUrl":null,"url":null,"abstract":"In this article, an adaptive neural network based controller for multi-degree-of-freedom robot manipulators with mixed rigid/flexible joints is investigated within frame of virtual decomposition control theory. First, virtual decomposition principle is introduced and applied to decouple the entire system of robot manipulator with mixed rigid/flexible joints into subsystems in terms of links and joints. Then, generalized neural networks are incorporated into the control law of rigid-link subsystems and rigid-joint subsystems to make the corresponding sub-control without invoing any model parameters. In addition, overall stability analysis of the control system is given according to the theory of virtual stability and Lyapunov stability. Finally, validation example is provided to verify the proposed control approach.","PeriodicalId":246050,"journal":{"name":"2021 WRC Symposium on Advanced Robotics and Automation (WRC SARA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Neural Network Based Virtual Decomposition Control for Robot Manipulator with Mixed Rigid/Flexible Joints\",\"authors\":\"Wei Xia, Huashan Liu\",\"doi\":\"10.1109/WRCSARA53879.2021.9612616\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, an adaptive neural network based controller for multi-degree-of-freedom robot manipulators with mixed rigid/flexible joints is investigated within frame of virtual decomposition control theory. First, virtual decomposition principle is introduced and applied to decouple the entire system of robot manipulator with mixed rigid/flexible joints into subsystems in terms of links and joints. Then, generalized neural networks are incorporated into the control law of rigid-link subsystems and rigid-joint subsystems to make the corresponding sub-control without invoing any model parameters. In addition, overall stability analysis of the control system is given according to the theory of virtual stability and Lyapunov stability. Finally, validation example is provided to verify the proposed control approach.\",\"PeriodicalId\":246050,\"journal\":{\"name\":\"2021 WRC Symposium on Advanced Robotics and Automation (WRC SARA)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 WRC Symposium on Advanced Robotics and Automation (WRC SARA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WRCSARA53879.2021.9612616\",\"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 WRC Symposium on Advanced Robotics and Automation (WRC SARA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WRCSARA53879.2021.9612616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Neural Network Based Virtual Decomposition Control for Robot Manipulator with Mixed Rigid/Flexible Joints
In this article, an adaptive neural network based controller for multi-degree-of-freedom robot manipulators with mixed rigid/flexible joints is investigated within frame of virtual decomposition control theory. First, virtual decomposition principle is introduced and applied to decouple the entire system of robot manipulator with mixed rigid/flexible joints into subsystems in terms of links and joints. Then, generalized neural networks are incorporated into the control law of rigid-link subsystems and rigid-joint subsystems to make the corresponding sub-control without invoing any model parameters. In addition, overall stability analysis of the control system is given according to the theory of virtual stability and Lyapunov stability. Finally, validation example is provided to verify the proposed control approach.