Road Condition Monitoring for Optimized Antilock Braking System

Abstract

The aim of an anti-lock braking system is to detect the locking of wheels and to adjust the breaking force accordingly so as to prevent this locking. This action provides improved stability and controlled steering during the application of vehicle brakes in an emergency. However, antilock braking suffers from a few drawbacks, i.e. the interaction between the road and the tire is nonlinear, the vehicle dynamics are complex and there is uncertainty of road conditions. Since it is difficult to obtain information about these uncertainties in real time directly, this is a serious research challenge to accurately identify road condition in real time. ABS used presently are based upon empirical formulation, their activation takes place when the slip ratio exceeds a preset threshold (usually 0.2). However, this mechanism is defective as the slip ratios for various road surfaces are different. Furthermore, sensor data is used by present ABS systems to determine the slip ratio. This measurement is susceptible to noise as slip ratio is usually a small quantity and accuracy of the computed value is therefore questionable. Moreover, aging, cost and maintenance of the sensors to prevent dust etc increase the likelihood of failure of the system. In order to overcome the limitation of sensor based ABS systems, In this a sensor less system has been proposed to accurately identify the optimum slip ratio in order to optimize the antilock braking system. In this research work an indirect approach is used in which variations in the dynamic response of the vehicle are investigated and from the dynamic variations optimum slip ratio is identified using a Fuzzy inference system. The dynamics of the vehicle are estimated using Kalman filtering approach. Effectiveness of proposed method is affirmed by the presented simulation results.