Neural Network Tracking of Switched Robotic Manipulators with Mode-Dependent Average Dwell Time

被引：0
|
作者：
Ma, Chao [1 ,2 ]
Wu, Wei [2 ]
Ji, Yidao [3 ]
Wang, Wei [3 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Mech Engn, Beijing 100083, Peoples R China
[2] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[3] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (IEEE-RCAR 2020) | 2020年
基金：
中国国家自然科学基金;
关键词：
STABILITY ANALYSIS; SYSTEMS; STABILIZATION;
D O I：
10.1109/rcar49640.2020.9303300
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper is concerned with the tracking control for robotic manipulators with switching loads and parameter uncertainties. More precisely, the neural network approximation method is adopted with adaptive control to deal with the unmodeled dynamics. Multiple Lyapunov function method is used based on mode-dependent average dwell time, such that the tracking error can converge with desired region. A numerical example is finally provided for illustrating our proposed tracking strategy.
引用
收藏
页码：399 / 403
页数：5
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