Abstract: In view of the nonlinear property of vehicle braking process and the complexity of on-line estimation, a method based on Burckhardt model to identify the optimal slip ratio on-line is proposed . Burckhardt model is firstly revised with three radial basis function neural networks (RBFNNs), each of which includes the parameters of road conditions. Hybrid parameter optimization method, a combination of particle swarm optimization (PSO) and structured nonlinear parameter optimization method (SNPOM), is implemented for parameter optimization of the designed three RBFNNs, resulting in Burckhardt model of any setting road condition, and optimal slip ratio identification system is investigated by utilizing the parameters of selected typical road conditions. The results of simulation studies in brake-by-wire (BBW) system validate the feasibility and flexibility of the method discussed.

Key words: Hybrid parameter optimization method Burckhardt model, Optimal slip-ratio, Radial basis function(RBF) neural network, Pulsating Flow, resonance frequency, self-resonating water jet, signal analysis