engleski

Tire/Road Friction Force Estimation in Automotive Control Systems

Good information about friction force generated in contact between wheel and road significant importance in many active safety systems in modern vehicles (anti-lock brake systems, traction control, vehicle dynamic systems, etc.). For this purpose two different approaches has been presented. First approach is based on modification of extended Kalman filter by augmenting it by integral action (PI Kalman filter), in order to estimate additional model parameter. Second estimation scheme is based on RBF neural network. 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. Quality of the estimation for both of estimators is examined through simulations using one wheel friction model.

Automotive Applications; Estimation Techniques; Kalman Filter; Neural Network; Lyapunov Stability

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