Journal article
Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine
Computers & Electrical Engineering, Vol.86, Article 106756
2020
Abstract
In this paper, a novel extreme-learning-machine (ELM)-based robust adaptive integral terminal sliding mode (AITSM) control strategy is developed for the precise tracking control of a steer-by-wire (SBW) system with uncertain dynamics. The proposed control not only ensures the finite-time error convergence but also effectively estimates the lumped uncertainty via a single-hidden layer feedforward network (SLFN) with ELM. Different from conventional ELM using least square optimization approach, the ELM in this work is designed to adaptively estimate the lumped uncertainty from the perspective of global stability of the closed-loop system. The stability of the closed-loop control system is proved in Lyapunov sense. Simulations are carried out to demonstrate the superior control performance of the proposed control.
Details
- Title
- Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine
- Authors/Creators
- M. Ye (Author/Creator) - Hefei University of TechnologyH. Wang (Author/Creator) - Murdoch University
- Publication Details
- Computers & Electrical Engineering, Vol.86, Article 106756
- Publisher
- Elsevier
- Identifiers
- 991005541089607891
- Copyright
- © 2020 Elsevier Ltd
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Journal article
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- 4 Electrical Engineering, Electronics & Computer Science
- 4.29 Automation & Control Systems
- 4.29.1251 Vehicle Dynamics Control
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