Application of the RBF neural network for tire-road friction estimation (CROSBI ID 491244)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Matuško, Jadranko ; Petrović, Ivan ; Perić, Nedjeljko
engleski
Application of the RBF neural network for tire-road friction estimation
This paper deals with the problem of the robust tire-road friction force estimation. Good information about friction force generated in contact between wheel and road has significant importance in many active safety systems in modern vehicles (anti-lock brake systems, traction control, vehicle dynamic systems, etc). Since state estimators are usually based on exact model of process, they are therefore limited by the model accuracy. A new estimation scheme based on RBF neural net-works is proposed in this paper. The neural network is added to the estimator to compensate the effects of the friction model uncertainties to the estimation quality. An adaptation law for the neural network parameters is derived using Lyapunov stability analysis. The proposed state estimator provides accurate estimation of the tire-road friction force when friction characteristic is only approximately known or even completely unknown. Quality of the estimation is examined through simulation using one wheel friction model. Simulation results suggest very fast compensation of the changes of the model parameters (< 150 ms) even when they vary in a wide range (changes of 100% and more). Possible drawback of proposed estimation scheme is the fact that neural network does not give the information what particular parameter has changed
Tire/road friction; RBF neural network; Lyapunov stability
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Podaci o prilogu
2003.
objavljeno
Podaci o matičnoj publikaciji
CD-ROM Proceedings of the IEEE International Symposium on Industrial Electronics, ISIE 2003
Rio de Janeiro:
Podaci o skupu
IEEE International Symposium on Industrial Electronics
predavanje
09.06.2003-11.06.2003
Rio de Janeiro, Brazil