Adaptive RBF-Neural-Network Control with Force Observer for Teleoperation Robotic System

doi:10.1007/978-981-99-2789-0_27

UM > Faculty of Science and Technology > DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING

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	Adaptive RBF-Neural-Network Control with Force Observer for Teleoperation Robotic System
	An,Zhaohui 1,2,3; Min,Gaochen 1,2,3; Jiang,Xiangzuo 4; Yu,Xinbo 1,2,3; He,Wei 1,2,3; Silvestre,Carlos5,6
	2023
Conference Name	1st International Conference on Cognitive Computation and Systems, ICCCS 2022
Source Publication	Communications in Computer and Information Science
Volume	1732 CCIS
Pages	315-330
Conference Date	17-18 Dec 2022
Conference Place	Beijing, China
Publisher	Springer Science and Business Media Deutschland GmbH
Abstract	In this paper, an adaptive radial basis function neural network (RBFNN) control strategy is proposed for bilateral teleoperation robotic system with uncertainty and time delay. Meantime, the force observer method is used to estimate the interaction force between the slave robot and environment. The model of teleoperation robotic system is analyzed, and then the uncertainty of bilateral teleoperation robotic system is estimated by using RBFNN. Using Lyapunov stability theorem, the stability of teleoperation robotic system under our proposed control is proved. Finally, the effectiveness of the proposed control algorithm is verified by Matlab Simulink, and position tracking and force estimation performance of the teleoperation robotic system are guaranteed.
Keyword	Adaptive Rbfnn Control Force Observer Teleoperation Robotic System Time Delay
DOI	10.1007/978-981-99-2789-0_27
URL	View the original
Language	英語English
Scopus ID	2-s2.0-85163404494
Fulltext Access	View Full-Text via DOI View Full-Text via Scopus
Citation statistics
Document Type	Conference paper
Collection	DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding Author	He,Wei
Affiliation	1.School of Intelligence Science and Technology,Institute of Artificial Intelligence,University of Science and Technology Beijing,Beijing,100083,China 2.Beijing Advanced Innovation Center for Materials Genome Engineering,University of Science and Technology Beijing,Beijing,100083,China 3.Key Laboratory of Intelligent Bionic Unmanned Systems,Ministry of Education,University of Science and Technology Beijing,Beijing,100083,China 4.Donald Bren School of Information and Computer Sciences,University of California,Irvine,92697,United States 5.Department of Electrical and Computer Engineering,Faculty of Science and Technology,University of Macau,Macao 6.Institute for Systems and Robotics (ISR/IST),LARSyS,Instituto Superior Técnico,Universidade de Lisboa,Lisbon,Portugal
Recommended Citation GB/T 7714	An,Zhaohui,Min,Gaochen,Jiang,Xiangzuo,et al. Adaptive RBF-Neural-Network Control with Force Observer for Teleoperation Robotic System[C]:Springer Science and Business Media Deutschland GmbH, 2023, 315-330.
APA	An,Zhaohui., Min,Gaochen., Jiang,Xiangzuo., Yu,Xinbo., He,Wei., & Silvestre,Carlos (2023). Adaptive RBF-Neural-Network Control with Force Observer for Teleoperation Robotic System. Communications in Computer and Information Science, 1732 CCIS, 315-330.

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