Literature DB >> 33293045

Adaptive slip ratio estimation for active braking control of high-speed trains.

Bin Chen1, Zhiwu Huang1, Rui Zhang1, Fu Jiang2, Weirong Liu3, Heng Li3, Jing Wang4, Jun Peng3.   

Abstract

Active braking control systems in high-speed trains are vital to ensure safety and are intended to reduce brake distances and prevent the wheels from locking. The slip ratio, which represents the relative difference between the wheel speed and vehicle velocity, is crucial to the design and successful implementation of active braking control systems. Slip ratio estimation and active braking control are challenging owing to the uncertainties of wheel-rail adhesion and system nonlinearities. Therefore, this paper proposes a novel adaptive slip ratio estimation approach for the active braking control based on an improved extended state observer. The extended state observer is developed through the augmentation of the system state-space to estimate the unmeasured train states as well as the model uncertainty. The accurate slip ratio is estimated using the observed extended states. Furthermore, the adaptability of the observer is improved by introducing the beetle antennae search algorithm to determine the optimal observer parameters. Finally, a feedback linearization braking control law is established to stabilize the closed-loop system due to its potential in coping with nonlinearities, which benefits the proven theoretical bounded stability. Experimental results validate the effectiveness of the proposed method.
Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Keywords:  Active braking control; Beetle antennae search; Extended state observer; Feedback linearization; Slip ratio estimation

Year:  2020        PMID: 33293045     DOI: 10.1016/j.isatra.2020.11.027

Source DB:  PubMed          Journal:  ISA Trans        ISSN: 0019-0578            Impact factor:   5.468


  1 in total

1.  Safety evaluation of rail transit vehicle system based on improved AHP-GA.

Authors:  Sihui Dong; Fei Yu; Kang Wang
Journal:  PLoS One       Date:  2022-08-24       Impact factor: 3.752

  1 in total

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