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

L. Chen; B. Yan; H. Wang; K. Shao; E. Kurniawan; G. Wang

doi:10.1016/j.conengprac.2022.105064

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

Journal article

Peer reviewed

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

L. Chen, B. Yan, H. Wang, K. Shao, E. Kurniawan and G. Wang

Control Engineering Practice, Vol.121, Art. 105064

2022

DOI: https://doi.org/10.1016/j.conengprac.2022.105064

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Abstract

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.

Details

Title: Extreme-learning-machine-based robust integral terminal sliding mode control of bicycle robot
Authors/Creators: L. Chen (Author/Creator) - Hangzhou Dianzi University
B. Yan (Author/Creator) - Hangzhou Dianzi University
H. Wang (Author/Creator) - Murdoch University
K. Shao (Author/Creator) - Tsinghua University
E. Kurniawan (Author/Creator) - Research Center for Physics, National Research and Innovation Agency, Tangerang Selatan 15314, Indonesia
G. Wang (Author/Creator) - Hangzhou Dianzi University
Publication Details: Control Engineering Practice, Vol.121, Art. 105064
Publisher: Elsevier
Identifiers: 991005542967607891
Copyright: © 2022 Elsevier Ltd.
Murdoch Affiliation: School of Engineering and Energy; Centre for Water, Energy and Waste
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.29 Automation & Control Systems; 4.29.104 Adaptive Control
Web Of Science research areas: Automation & Control Systems; Engineering, Electrical & Electronic
ESI research areas: Engineering