Adaptive Teleoperation Control Method Based on RBF-Neural Networks and Performance Analysis

被引：0

作者：

Cheng, Ruizhou ^{[1
]}

Huang, Panfeng ^{[1
]}

Lu, Zhenyu ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Astronaut, Res Ctr Intelligent Robot, Natl Key Lab Aerosp Flight Dynam, 127 West Youyi Rd, Xian, Shaanxi, Peoples R China

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (RCAR) | 2017年

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work presents an adaptive teleoperation control method based on RBF-Neural Networks. First, the model of the teleoperation system with two slave robots is built. Then the controllers of the slaves and masters are designed separately. For the slave side, the dynamic uncertainties are considered as the main factor to influence the system stability, which is estimated by the RBF-Neural Networks (RBF-NNs). The structure parameters of the masters are known before the operation. Furthermore, we discuss the system stable conditions and position tracking effect of the slaves to the maters' motions. The proofs reveal that the system will converge to the stable states based on the assumptions that the estimating errors are smaller than a threshold value. The final tracking errors are corresponding with bounding values of the estimating errors of RBF-NNs method. Finally, a simulation is taken to certify the effectiveness of proposed method and the main conclusions.

引用

页码：493 / 498

页数：6

共 50 条

[1] RBF-neural network adaptive control of mobile manipulator
Qian, Yang
Wu, Xiongjun
Wu, Shengtong
Han, Fei
[J]. 2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 5639 - 5646
[2] Effective training algorithms for RBF-neural networks
Ososkov, G
Stadnik, A
[J]. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2003, 502 (2-3): : 529 - 531
[3] An New Adaptive Digital Predistortion Technology Based on RBF-Neural Network
Qiu Wei
Zhu Minghua
Xie Huijun
[J]. PROCEEDINGS OF 2010 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND INDUSTRIAL ENGINEERING, VOLS I AND II, 2010, : 953 - 956
[4] Adaptive control on manifolds with RBF neural networks
Terekhov, VA
Tyukin, IY
Prokhorov, DV
[J]. PROCEEDINGS OF THE 39TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5, 2000, : 3831 - 3836
[5] Application of adaptive PID based on RBF neural networks in temperature control
Yu Meng
Zou Zhiyun
Ren Fujian
Pan Yusong
Gai Xijie
[J]. 2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2014, : 4302 - 4306
[6] Adaptive control based on RBF networks
Chen, XH
Gao, F
Qian, JX
[J]. PROCEEDINGS OF THE 35TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4, 1996, : 3810 - 3815
[7] Adaptive Robust Control based on RBF Neural Networks for Duct Cleaning Robot
Bu Dexu
Wei, Sun
Yu Hongshan
Cong, Wang
Hui, Zhang
[J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2015, 13 (02) : 475 - 487
[8] Sliding Mode Control of ROV Based on RBF Neural Networks Adaptive Learning
Liu, Heping
Gong, Zhenbang
Li, Min
[J]. 2008 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEM AND KNOWLEDGE ENGINEERING, VOLS 1 AND 2, 2008, : 590 - +
[9] Adaptive robust control based on RBF neural networks for duct cleaning robot
Bu Dexu
Sun Wei
Yu Hongshan
Wang Cong
Zhang Hui
[J]. International Journal of Control, Automation and Systems, 2015, 13 : 475 - 487
[10] RBF neural networks based robot non-smooth adaptive control
Zhao Dongya
Zhu Quanmin
Li Shaoyuan
[J]. 2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 583 - 587

← 1 2 3 4 5 →