Logo image
Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine
Journal article   Peer reviewed

Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine

M. Ye and H. Wang
Computers & Electrical Engineering, Vol.86, Article 106756
2020
url
Link to Published Version *Subscription may be requiredView

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

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#11 Sustainable Cities and Communities

Source: InCites

Metrics

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.29 Automation & Control Systems
4.29.1251 Vehicle Dynamics Control
Web Of Science research areas
Computer Science, Hardware & Architecture
Computer Science, Interdisciplinary Applications
Engineering, Electrical & Electronic
ESI research areas
Computer Science
Logo image