Journal article
Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine
Neural Computing and Applications
2021
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.
Details
- Title
- Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine
- Authors/Creators
- Y. Hu (Author/Creator)H. Wang (Author/Creator)A. Yazdani (Author/Creator)Z. Man (Author/Creator)
- Publication Details
- Neural Computing and Applications
- Publisher
- Springer London
- Identifiers
- 991005543606707891
- Copyright
- © 2021 Springer Nature Switzerland AG.
- Murdoch Affiliation
- School of Engineering and Energy; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
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Source: InCites
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.29 Automation & Control Systems
- 4.29.104 Adaptive Control
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- ESI research areas
- Engineering