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Friction condition characterization for rail vehicle advanced braking system

journal contribution
posted on 2020-02-19, 00:00 authored by Sundar Shrestha, Maksym SpiryaginMaksym Spiryagin, Qing WuQing Wu
This paper discusses the improvement in rail vehicle braking systems and proposes a new control algorithm for optimum utilization of the available adhesion between wheel and rail during braking conditions. Unlike conventional controllers, the proposed controller algorithm is responsive to the change of operational and environmental parameters between wheel and rail. It is designed based on multiple mode shifting during operations as the parameters change. Furthermore, adhesion is a process which cannot be measured directly; it has to be estimated from related measurable parameters and can be expressed in a quantitative term such as adhesion coefficient. Thus, an online observer to estimate the available adhesion is also proposed in this paper. The observer uses feedback responses to compensate for any error and ensures better accuracy by overcoming steady-state fluctuations and an impractical initial error. For numerical simulation, a wagon model considering longitudinal dynamics is developed. The adhesion force is then modelled by a proper definition of an adhesion-creep characteristics curve that was achieved from the experimental data collected from field measurements. Two sets of simulation were carried out. In the first simulation, the performance of the proposed observer was compared with the existing observer under different adhesion conditions. The comparison suggests that the proposed observer estimates realistic results even under different adhesion conditions as well as during switching between these conditions. In the second simulation, the performance of the proposed control algorithm was compared with the conventional algorithm. The comparison suggests that the proposed control algorithm is able to optimally utilize the available adhesion between wheel and rail to ensure the shortest possible braking distance while maintaining vehicle stability. © 2019 Elsevier Ltd

Funding

Category 3 - Industry and Other Research Income

History

Volume

134

Start Page

1

End Page

16

Number of Pages

16

eISSN

1096-1216

ISSN

0888-3270

Publisher

Academic Press, UK

Peer Reviewed

  • Yes

Open Access

  • No

Acceptance Date

2019-08-21

Author Research Institute

  • Centre for Railway Engineering

Era Eligible

  • Yes

Journal

Mechanical Systems and Signal Processing

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