Vehicle Sideslip Estimation for Four-Wheel-Steering Vehicles Using a Particle Filter

The availability of the most relevant vehicle states is crucial for the development of advanced vehicle control systems and driver assistance systems. Specifically the vehicle sideslip angle plays a key role, yet this state is unpractical to measure and still not straightforward to estimate. This paper investigates a particle filter approach to estimate the chassis sideslip angle of road vehicles. The filter relies on a physical model of the vehicle and on measurements available from cheap and widespread sensors including inertial measurement unit and steering wheel angle sensor(s). The approach is validated using experimental data collected with the research platform RoboMobil (RoMo), a by-wire electric vehicle with wheel-individual traction and steering actuators. Results show that the performance of the proposed particle filter is satisfactory, and indicate directions for further improvement.

• Record URL:
  https://doi.org/10.1007/978-3-030-38077-9
• Record URL:
  https://link.springer.com/chapter/10.1007/978-3-030-38077-9_185
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9783030380762
• Supplemental Notes:
  • © Springer Nature Switzerland AG 2020.
• Corporate Authors:

  Springer International Publishing

  ,    
• Authors:
  • Lenzo, Basilio
  • De Castro, Ricardo
• Conference:
  • 26th Symposium of the International Association of Vehicle System Dynamics (IAVSD 2019)
  • Location: Gothenburg , Sweden
  • Date: 2019-8-12 to 2019-8-16
• Publication Date: 2020-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1624-1634
• Monograph Title: Advances in Dynamics of Vehicles on Roads and Tracks: Proceedings of the 26th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2019, August 12-16, 2019, Gothenburg, Sweden
• Serial:
  • Lecture Notes in Mechanical Engineering
  • Publisher: Springer International Publishing
  • ISSN: 2195-4356
  • EISSN: 2195-4364
  • Serial URL: http://link.springer.com/bookseries/11236

Subject/Index Terms

• TRT Terms: Algorithms; Electric vehicles; Four wheel steering; Lateral acceleration; Monte Carlo method; Rear wheel drive
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01904091
• Record Type: Publication
• ISBN: 9783030380762
• Files: TRIS
• Created Date: Jan 4 2024 10:52AM