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Vehicle Sideslip Angle Estimation Through Neural Networks: Application to Experimental Data

[+] Author Affiliations
Stefano Melzi, Edoardo Sabbioni, Alessandro Concas

Politecnico di Milano, Milano, Italy

Marco Pesce

Centro Ricerche Fiat, Orbassano, Torino, Italy

Paper No. ESDA2006-95451, pp. 219-224; 6 pages
  • ASME 8th Biennial Conference on Engineering Systems Design and Analysis
  • Volume 2: Automotive Systems, Bioengineering and Biomedical Technology, Fluids Engineering, Maintenance Engineering and Non-Destructive Evaluation, and Nanotechnology
  • Torino, Italy, July 4–7, 2006
  • ISBN: 0-7918-4249-5 | eISBN: 0-7918-3779-3
  • Copyright © 2006 by ASME


This work explores the possibility of using a non-structured algorithm as a sideslip angle valuer: on the basis of a preliminary numerical analysis, a neural network was designed and trained with experimental signals of lateral acceleration, vehicle speed, yaw rate and steer angle. The network was applied to experimental data in order to verify its capability of self-adaptation to changes in friction coefficient and to provide accurate estimations for manoeuvres sensibly different from the ones used during the training stage. The simple architecture joined with an appropriate training set conferred good self-adaptation properties to the neural network which was able to provide satisfying estimation of side slip angle for a wide range of manoeuvres and different friction conditions.

Copyright © 2006 by ASME



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