Extreme-learning-machine-based robust integral terminal sliding mode control of bicycle robot

被引：31

作者：

Chen, Long ^{[1
]}

Yan, Bin ^{[1
]}

Wang, Hai ^{[2
]}

Shao, Ke ^{[3
]}

Kurniawan, Edi ^{[4
]}

Wang, Guangyi ^{[1
]}

机构：

[1] Hangzhou Dianzi Univ, Sch Elect & Informat, Hangzhou 310018, Peoples R China

[2] Murdoch Univ, Ctr Water Energy & Waste, Discipline Engn & Energy, Perth, WA 6150, Australia

[3] Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China

[4] Natl Res & Innovat Agcy, Res Ctr Phys, Tangerang Selatan 15314, Indonesia

来源：

CONTROL ENGINEERING PRACTICE | 2022年 / 121卷

基金：

中国国家自然科学基金;

关键词：

Balancing; Integral terminal sliding mode (ITSM); Bicycle robot (BR); Extreme learning machine (ELM); ELECTRONIC THROTTLE; AUTONOMOUS BICYCLE; STABILIZATION; SPACECRAFT; OBSERVER; SYSTEMS;

D O I：

10.1016/j.conengprac.2022.105064

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an extreme-learning-machine (ELM)-based robust integral terminal sliding mode (ITSM) control scheme is developed for a bicycle robot (BR) to achieve balancing target. First, the bicycle robot equipped with a reaction wheel is formulated by a second-order mathematical model with uncertainties. Then, an ITSM controller is designed for the balancing control of the BR, where an ELM scheme is designed as a compensator for estimating lumped uncertainties of the system. The stability proof of the closed-loop control system is presented based on Lyapunov theory. Comparative experimental results are demonstrated to verify the superior balancing performance of the proposed control.

引用

页数：10

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