Application of Neural Networks in the Estimation of Tire/Road Friction Using the Tire as Sensor

971122

02/24/1997

Event
SAE International Congress and Exposition
Authors Abstract
Content
The importance of friction between tire and road for the dynamic behavior of road vehicles has been emphasized in many publications. Continuously updated knowledge of the friction potential and the friction demand can help to improve maneuverability and thereby safety of vehicles under slippery road conditions. An on line estimation method, based on combination of side force and self aligning torque, generated by the tire, is theoretically founded on a simple brush type tire model.
The system is implemented in the front wheel suspension of a passenger car. To cope with the highly non-linear behavior of the wheel suspension and the actual tire, various static neural networks have been applied in the estimation procedure. Experiments have been carried out both in simulation using a full vehicle multi-body model and with an actual vehicle. Conclusions are drawn regarding the estimation principle, the application of neural networks and the implementation in a test vehicle.
Meta TagsDetails
DOI
https://doi.org/10.4271/971122
Pages
9
Citation
Pasterkamp, W., and Pacejka, H., "Application of Neural Networks in the Estimation of Tire/Road Friction Using the Tire as Sensor," SAE Technical Paper 971122, 1997, https://doi.org/10.4271/971122.
Additional Details
Publisher
Published
Feb 24, 1997
Product Code
971122
Content Type
Technical Paper
Language
English