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

Chen, L., Yan, B., Wang, H.ORCID: 0000-0003-2789-9530, Shao, K., Kurniawan, E. and Wang, G. (2022) Extreme-learning-machine-based robust integral terminal sliding mode control of bicycle robot. Control Engineering Practice, 121 . Art. 105064.

Link to Published Version: 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.

Item Type: Journal Article
Murdoch Affiliation(s): Engineering and Energy
Centre for Water, Energy and Waste
Publisher: Elsevier
Copyright: © 2022 Elsevier Ltd.
URI: http://researchrepository.murdoch.edu.au/id/eprint/63789
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