Freight train speed active disturbance rejection tracking and wheel anti-slip based on fuzzy neural network with DBO optimization-Reference-Cited by-同舟云学术

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Freight train speed active disturbance rejection tracking and wheel anti-slip based on fuzzy neural network with DBO optimization

Published:2023-09-09 Issue: Volume: Page:
ISSN:0948-7921
Container-title:Electrical Engineering
language:en
Short-container-title:Electr Eng

Author:

Yi LingZhi,Jiang WenBo,Yi Yu,Li JianLin,Xie Cheng

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Electrical and Electronic Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00202-023-02008-w.pdf

Reference31 articles.

1. Dick CT et al (2021) Quantifying recent trends in class 1 Freight railroad train length and weight by train type. Transp Res Rec 2675(12):890–903

2. Chen, X., Reconnecting Eurasia: a new logistics state, the China–Europe freight train, and the resurging ancient city of Xi’an. Eurasian Geography and Economics, 2021: p. 1–29.

3. Wu P, Wang Q-Y, Feng X-Y (2015) Automatic train operation based on adaptive terminal sliding mode control. Int J Autom Comput 12(2):142–148

4. Moaveni B, Fathabadi FR, Molavi A (2020) Supervisory predictive control for wheel slip prevention and tracking of desired speed profile in electric trains. ISA Trans 101:102–115

5. Cheng RJ et al (2019) Intelligent safe driving methods based on hybrid automata and ensemble CART algorithms for multihigh-speed trains. IEEE Trans Cybern 49(10):3816–3826

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1. A 4-Phase Combined Adhesion Threshold Algorithm for Wheel Slide Protection Systems in Rail Vehicles;Actuators;2024-01-25

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