Universal approximation using radial-basis-function networks
Neural Computation
Slip-based tire-road friction estimation
Automatica (Journal of IFAC)
Fast learning in networks of locally-tuned processing units
Neural Computation
Approximation capability in C(R¯n) by multilayer feedforward networks and related problems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
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This paper deals with the problem of robust tire/road friction force estimation. Availability of actual value of the friction force generated in contact between the tire and the road has significant importance for active safety systems in modern cars, e.g. anti-lock brake systems, traction control systems, vehicle dynamic systems, etc. Since state estimators are usually based on the process model, they are sensitive to model inaccuracy. In this paper we propose a new neural network based estimation scheme, which makes friction force estimation insensitive to modelling inaccuracies. The neural network is added to the estimator in order to compensate 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 friction force estimation and compensation of the changes of the model parameters even when they vary in wide range.