Guangzhu Peng;Tao Li;Chenguang Yang;C. L. Philip Chen
{"title":"基于近似值的机器人与环境交互导纳控制与性能保证","authors":"Guangzhu Peng;Tao Li;Chenguang Yang;C. L. Philip Chen","doi":"10.1109/TSMC.2024.3430265","DOIUrl":null,"url":null,"abstract":"Humans are able to compliantly interact with the environment by adapting its motion trajectory and contact force. Robots with the human versatility can perform contact tasks more efficiently with high motion precision. Motivated by multiple capabilities, we develop an approximation-based admittance control strategy that adapts and tracks the trajectory with guaranteed performance for the robots interacting with unknown environments. In this strategy, the robot can adapt and compensate its feedforward force and stiffness to interact with the unknown environment. In particular, a reference trajectory is generated through the admittance control to achieve a desired interaction level. To improve the interaction performance, a tracking error bound for both the transient and steady states is prespecified, and a controller is designed to ensure the tracking control performance. In the presence of unknown robot dynamics, neural networks are integrated into tracking controller to compensate uncertainties. The stability and convergence conditions of the closed-loop system are analysed by the Lyapunov theory. The effectiveness of the proposed control method is demonstrated on the Baxter robot.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Approximation-Based Admittance Control of Robot-Environment Interaction With Guaranteed Performance\",\"authors\":\"Guangzhu Peng;Tao Li;Chenguang Yang;C. L. Philip Chen\",\"doi\":\"10.1109/TSMC.2024.3430265\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Humans are able to compliantly interact with the environment by adapting its motion trajectory and contact force. Robots with the human versatility can perform contact tasks more efficiently with high motion precision. Motivated by multiple capabilities, we develop an approximation-based admittance control strategy that adapts and tracks the trajectory with guaranteed performance for the robots interacting with unknown environments. In this strategy, the robot can adapt and compensate its feedforward force and stiffness to interact with the unknown environment. In particular, a reference trajectory is generated through the admittance control to achieve a desired interaction level. To improve the interaction performance, a tracking error bound for both the transient and steady states is prespecified, and a controller is designed to ensure the tracking control performance. In the presence of unknown robot dynamics, neural networks are integrated into tracking controller to compensate uncertainties. The stability and convergence conditions of the closed-loop system are analysed by the Lyapunov theory. The effectiveness of the proposed control method is demonstrated on the Baxter robot.\",\"PeriodicalId\":48915,\"journal\":{\"name\":\"IEEE Transactions on Systems Man Cybernetics-Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Systems Man Cybernetics-Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10620689/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Systems Man Cybernetics-Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10620689/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Approximation-Based Admittance Control of Robot-Environment Interaction With Guaranteed Performance
Humans are able to compliantly interact with the environment by adapting its motion trajectory and contact force. Robots with the human versatility can perform contact tasks more efficiently with high motion precision. Motivated by multiple capabilities, we develop an approximation-based admittance control strategy that adapts and tracks the trajectory with guaranteed performance for the robots interacting with unknown environments. In this strategy, the robot can adapt and compensate its feedforward force and stiffness to interact with the unknown environment. In particular, a reference trajectory is generated through the admittance control to achieve a desired interaction level. To improve the interaction performance, a tracking error bound for both the transient and steady states is prespecified, and a controller is designed to ensure the tracking control performance. In the presence of unknown robot dynamics, neural networks are integrated into tracking controller to compensate uncertainties. The stability and convergence conditions of the closed-loop system are analysed by the Lyapunov theory. The effectiveness of the proposed control method is demonstrated on the Baxter robot.
期刊介绍:
The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.