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.
*Subscription may be required
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 |
![]() |
Item Control Page |