Improvement of vehicle stability using a controller based on reinforcement learning

This paper presents a study on the use of reinforcement learning to control the torque vectoring of a small electric racecar aiming to improve vehicle handling and vehicle stability. The reinforcement-learning algorithm used is Neural Fitted Q Iteration, and the sampling of experiences is based on simulations using the software CarMaker. The cost function is based on the position of the states on the phase-plane of sideslip angle and angular velocity. To investigate the maximum ratio of torque distribution that should be set to guarantee stability as well as the effectiveness of the controller inputs in the learning process, two experiments were done (A and B) with different states and possibilities of torque distribution. The controller A is able to improve the vehicle handling and stability with a significant reduction in vehicle sideslip angle. And it also showed that 70% of torque distribution is enough to keep the vehicle stable.

• Record URL:
  • https://doi.org/10.1504/IJVSMT.2020.111677
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/17456436
• Supplemental Notes:
  • Copyright © 2020 Inderscience Enterprises Ltd.
• Authors:
  • Amaral, Janaína R
  • Fiorentin, Thiago A
  • Göllinger, Harald
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 97-112
• Serial:
  • International Journal of Vehicle Systems Modelling and Testing
  • Volume: 14
  • Issue Number: 2-3
  • Publisher: Inderscience Enterprises Limited
  • ISSN: 1745-6436
  • EISSN: 1745-6444
  • Serial URL: http://www.inderscience.com/jhome.php?jcode=IJVSMT

Subject/Index Terms

• TRT Terms: Automatic controllers; Driving; Electric vehicles; Machine learning; Race cars; Torque
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01763137
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 3 2021 2:23PM