Online Estimation of the Adhesion Coefficient and Its Derivative Based on the Cascading SMC Observer
Joint Authors
Zhang, Chang-fan
He, Jing
Sun, Jian
Liu, Linfan
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-03
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The adhesion coefficient of wheel-rail surface cannot be directly measured, so a cascaded sliding-mode observer is proposed to observe adhesion coefficient and its derivative.
A kinetic model for running heavy-duty locomotive is also established.
The state equation of wheel adhesion control system is derived from the equation of traction motor torque balance, and adhesion coefficient is proposed to be calculated by load torque.
Then, the cascaded sliding-mode observer is designed, and its stability is justified by Lyapunov stability.
Based on the equivalence control principle for sliding-mode variable structure, an algorithm to estimate adhesion coefficient and its derivative is established.
The simulation and experimental results are used to verify the effectiveness of the observer with load variations or wheel-rail status changes.
American Psychological Association (APA)
Zhang, Chang-fan& Sun, Jian& He, Jing& Liu, Linfan. 2017. Online Estimation of the Adhesion Coefficient and Its Derivative Based on the Cascading SMC Observer. Journal of Sensors،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1187502
Modern Language Association (MLA)
Zhang, Chang-fan…[et al.]. Online Estimation of the Adhesion Coefficient and Its Derivative Based on the Cascading SMC Observer. Journal of Sensors No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1187502
American Medical Association (AMA)
Zhang, Chang-fan& Sun, Jian& He, Jing& Liu, Linfan. Online Estimation of the Adhesion Coefficient and Its Derivative Based on the Cascading SMC Observer. Journal of Sensors. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1187502
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187502