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Extreme-learning-machine-based robust AITSM control for steer-by-wire systems

Ye, M., Wang, H.ORCID: 0000-0003-2789-9530, Cao, Z., Zheng, J., Man, Z. and Jin, X. (2019) Extreme-learning-machine-based robust AITSM control for steer-by-wire systems. In: Chinese Control Conference (CCC) 2019, 27 - 30 July 2019, Guangzhou, China

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In this paper, a robust adaptive integral terminal sliding mode (AITSM) control strategy for the steer-by-wire (SbW) systems with uncertain dynamics is presented. The proposed control strategy consists of an AITSM controller and an extreme-learning-machine (ELM)-based compensator, where the ELM as a single-hidden layer feedforward neural network (SLFN) is adopted to adaptively learn the lumped uncertainty in Lyapunov sense aiming at effectively eliminating the effects of uncertainties in the closed-loop system. A novel adaptive integral terminal sliding surface is used to force the closed-loop system to start on the sliding surface at the very beginning compared with the traditional sliding mode (SM)-based SbW control strategies, in the sense that the robust steering performance against parameter variations and road disturbances is ensured. The proof of the closed loop system stability based on Lyapunov theory is demonstrated in detail. The simulation results are presented in support of the superior performance and effectiveness of the proposed control.

Item Type: Conference Paper
Murdoch Affiliation(s): College of Science, Health, Engineering and Education
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