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Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine

Hu, Y., Wang, H.ORCID: 0000-0003-2789-9530, Yazdani, A. and Man, Z. (2021) Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine. Neural Computing and Applications .

Link to Published Version: https://doi.org/10.1007/s00521-021-06365-0
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Abstract

This paper proposes a novel extreme learning machine (ELM)-based fixed time adaptive trajectory control for electronic throttle valve system with uncertain dynamics and external disturbances. The developed control strategy consists of a recursive full order terminal sliding mode structure based on the bilimit homogeneous property and a lumped uncertainty changing rate upper bound estimator via an adaptive ELM algorithm such that not only the fixed time convergence for both sliding variable and error states can be guaranteed, but also the chattering phenomenon can be suppressed effectively. The stability of the closed-loop system is proved rigorously based on Lyapunov theory. The simulation results are given to verify the superior tracking performance of the proposed control strategy.

Item Type: Journal Article
Murdoch Affiliation(s): Engineering and Energy
Centre for Water, Energy and Waste
Publisher: Springer London
Copyright: © 2021 Springer Nature Switzerland AG.
URI: http://researchrepository.murdoch.edu.au/id/eprint/61906
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