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

[+] Author Affiliations
Stefano Melzi, Ferruccio Resta, Edoardo Sabbioni

Politecnico di Milano, Milano, Italy

Paper No. ESDA2006-95376, pp. 167-172; 6 pages
doi:10.1115/ESDA2006-95376
From:
  • 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

abstract

Aim of this paper is to evaluate the possibility of estimating the vehicle sideslip angle through a non-structured algorithm based on neural networks. Results reported are relevant to a numerical investigation of the network performance which can be regarded as preliminary stage for the application on a real vehicle. A numerical model is used to describe the vehicle dynamics and to generate the inputs for the neural network; with an appropriate set of manoeuvres for network training the non-structured algorithm provides reliable results when applied to a complete series of handling manoeuvres carried out with different tire-road friction coefficients.

Copyright © 2006 by ASME

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