An Intelligent Decision Model for Traction Control in Speed Sensor Vehicles

In this study, an intelligent decision model based on reliability measures is proposed for the traction control system of the speed sensor vehicles. This model is formulated for the integrated and complex system based on Bayesian Decision Theory that is used to model pattern recognition of train traction conditions along with high pre cision and manageable number of; state natures (i.e., spin/slip, normal, and slide), f eatures (delta speed and train speed) and having the prior knowledge traction conditions. The intelligent model is useful to alleviate the impact of the noisy sensors (inaccurate data) and its delays in such a hard real-time control system. The model s engine involves mathematical problem which can be solved in any programming language in on-board or embedded computers. The conceptual model is applied to a hypothetical case study with promising results for target and simulation systems.