A novel anti-slip control approach for railway vehicles with traction based on adhesion estimation with swarm intelligence

Anti-slip control systems are essential for railway vehicle systems with traction. In order to propose an effective anti-slip control system, adhesion information between wheel and rail can be useful. However, direct measurement or observation of adhesion condition for a railway vehicle in operation is quite demanding. Therefore, a proportional–integral controller, which operates simultaneously with a recently proposed swarm intelligence-based adhesion estimation algorithm, is proposed in this study. This approach provides determination of the adhesion optimum on the adhesion-slip curve so that a reference slip value for the controller can be determined according to the adhesion conditions between wheel and rail. To validate the methodology, a tram wheel test stand with an independently rotating wheel, which is a model of some low floor trams produced in Czechia, is considered. Results reveal that this new approach is more effective than a conventional controller without adhesion condition estimation.

• Record URL:
  https://doi.org/10.1007/s40534-020-00223-w
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/26624745
• Supplemental Notes:
  • © 2020 Abdulkadir Zirek and Altan Onat.
• Authors:
  • Zirek, Abdulkadir
  • 0000-0002-6670-134X
  • Onat, Altan
• Publication Date: 2020-11

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Photos; References; Tables;
• Pagination: pp 346–364
• Serial:
  • Railway Engineering Science
  • Volume: 28
  • Issue Number: 4
  • Publisher: Springer Nature
  • ISSN: 2662-4745
  • Serial URL: https://www.springer.com/journal/40534

Subject/Index Terms

• TRT Terms: Adhesion; Car wheels (Railroads); Control systems; Rolling contact; Slip resistance; Traction
• Geographic Terms: Czech Republic
• Subject Areas: Railroads; Vehicles and Equipment;

Filing Info

• Accession Number: 01760502
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 6 2020 3:10PM