{"title":"基于神经网络的机器人机械手位置与力控制","authors":"Yu Zhao, C. Cheah","doi":"10.1109/RAMECH.2004.1438935","DOIUrl":null,"url":null,"abstract":"Most research on force control of robot manipulators has assumed that the kinematics and constraint surface are known exactly. In this paper, the position and force control problem of robots with uncertain kinematics, dynamics and constraint is addressed. An adaptive set point control law based on neural networks is proposed. Sufficient conditions for choosing the feedback gains are presented to guarantee the stability. Simulation results are presented to demonstrate the effectiveness of the proposed controller.","PeriodicalId":252964,"journal":{"name":"IEEE Conference on Robotics, Automation and Mechatronics, 2004.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Position and force control of robot manipulators using neural networks\",\"authors\":\"Yu Zhao, C. Cheah\",\"doi\":\"10.1109/RAMECH.2004.1438935\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most research on force control of robot manipulators has assumed that the kinematics and constraint surface are known exactly. In this paper, the position and force control problem of robots with uncertain kinematics, dynamics and constraint is addressed. An adaptive set point control law based on neural networks is proposed. Sufficient conditions for choosing the feedback gains are presented to guarantee the stability. Simulation results are presented to demonstrate the effectiveness of the proposed controller.\",\"PeriodicalId\":252964,\"journal\":{\"name\":\"IEEE Conference on Robotics, Automation and Mechatronics, 2004.\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Conference on Robotics, Automation and Mechatronics, 2004.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAMECH.2004.1438935\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Conference on Robotics, Automation and Mechatronics, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAMECH.2004.1438935","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Position and force control of robot manipulators using neural networks
Most research on force control of robot manipulators has assumed that the kinematics and constraint surface are known exactly. In this paper, the position and force control problem of robots with uncertain kinematics, dynamics and constraint is addressed. An adaptive set point control law based on neural networks is proposed. Sufficient conditions for choosing the feedback gains are presented to guarantee the stability. Simulation results are presented to demonstrate the effectiveness of the proposed controller.
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