Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network

In this work, a reliable and effective method to predict the vehicle side-slip angle is given by means of an artificial neural network. It is well known that artificial neural networks are a very powerful modelling tool. They are largely used in many engineering fields to model complex and strongly...

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Bibliographic Details
Main Authors: Chindamo, Daniel, Gadola, Marco
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Computer simulation
Linear systems
Neural networks
Nonlinear systems
Slip
Test procedures
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Summary:In this work, a reliable and effective method to predict the vehicle side-slip angle is given by means of an artificial neural network. It is well known that artificial neural networks are a very powerful modelling tool. They are largely used in many engineering fields to model complex and strongly non-linear systems. For this application, the network has to be as simple as possible in order to work in real-time within built-in applications such as active safety systems. The network has been trained with the data coming from a custom manoeuvre designed in order to keep the method simple and light from the computational point of view. Therefore, a 5-10-1 (input-hidden-output layer) network layout has been used. These aspects allow the network to give a proper estimation despite its simplicity. The proposed methodology has been tested by means of the CarSim ® simulation package, which is considered one of the reference tools in the field of vehicle dynamics simulation. To prove the effectiveness of the method, tests have been carried out under different adherence conditions.
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/201816602001