OS2-4 Neuro-Adaptive Control of High-Speed Trains under Uncertain Wheel-Rail Relationship

Title:	OS2-4 Neuro-Adaptive Control of High-Speed Trains under Uncertain Wheel-Rail Relationship
Publication:	ICAROB2020
Volume:	25
Pages:	706-711
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2020.OS2-4
Author(s):	Zhechen Wang, Yingmin Jia
Publication Date:	January 13, 2020
Keywords:	Train velocity tracking, adaptive control, wheel-rail relationship, barrier Lyapunov function
Abstract:	Traditional automatic controller designing in train systems is almost based on urban rail transit where the influence of changing wheel-rail relationship caused by the variation of speed and environment is ignored. However, highspeed railway operates in more open environment and higher speed, which leading to a more complex variation of wheel-rail relationship occurring. In this paper, we design an automatic train controller in high-speed railway which can realize the automatic velocity tracking even if the uncertain and nonlinear variation of complex wheel-rail relationship happens. First of all, the train dynamic model is established where the wheel-rail relationship is expressed as an uncertain unknown function and the train operation system is expressed as a third-order nonlinear system. Then, a neural network adaptive controller is designed by using the backstepping method and barrier Lyapunov function. Based on this controller, position and velocity tracking errors are semi-globally uniformly ultimate boundedness. Finally, the effectiveness of the algorithm is verified by simulation experiments.
PDF File:	https://alife-robotics.co.jp/members2020/icarob/data/html/data/OS/OS2/OS2-4.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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