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Robust fast nonsingular terminal sliding mode control strategy for electronic throttle based on extreme learning machine

Hu, Y., Wang, H.ORCID: 0000-0003-2789-9530, Cao, Z., Man, Z., Yu, M. and Ping, Z. (2019) Robust fast nonsingular terminal sliding mode control strategy for electronic throttle based on extreme learning machine. In: Chinese Control Conference (CCC) 2019, 27 - 30 July 2019, Guangzhou, China

Link to Published Version: https://doi.org/10.23919/ChiCC.2019.8866147
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Abstract

This paper proposes an extreme-learning-machine-based robust fast nonsingular terminal sliding mode control (FNTSMC) strategy for an electronic throttle (ET) system. Distinguished from the conventional implementations of sliding mode control (SMC), the prior knowledge of disturbance bound is not required but estimated by the novel neural networks titled as extreme learning machine (ELM) which features in the fast learning rate and excellent generalization. The unique of the proposed control strategy lies on that both the sliding variable and system state enjoy a finite-time convergence without the information of predetermined bound of system nonlinearities and disturbances. The comparative simulations are conducted to verify the effectiveness and robustness of the proposed control strategy.

Item Type: Conference Paper
Murdoch Affiliation: College of Science, Health, Engineering and Education
URI: http://researchrepository.murdoch.edu.au/id/eprint/52819
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