Performance Of Antilock Braking System Research Articles

Influence of Tire Operating Conditions on ABS Performance

ABSTRACT During its normal service life, a tire is subjected to large variations in operating conditions, such as ambient temperature, inflation pressure, and changes in tread depth. The longitudinal force response of the tire changes significantly because of each of these operating conditions. This, in turn, would directly influence the performance of the antilock braking system (ABS) installed in a vehicle. Current ABS systems are tuned for a vehicle with fixed operating thresholds that do not change. The objective of this study was to understand the influence of a tire's operating conditions on ABS efficiency and the extent of variation it can cause on stopping distance. This was done by obtaining longitudinal-slip characteristics for a given tire at various temperatures, inflation pressures, and tread depth through a traction trailer. These data were then used to simulate an ABS braking maneuver using a half-car vehicle model. The major reasons for the loss in stopping distance performance because of a drop in efficiency under each condition was then analyzed in detail. The latter part of this study explored the potential for improvement in stopping distance that could possibly be achieved through an intelligent ABS system that would use tire-sensed information, such as temperature, pressure, and tread depth to calculate essential tire characteristics in real time using an adaptive magic formula and change its tuning parameters accordingly.

Intelligent Hybrid Control System Design for Antilock Braking Systems Using Self-Organizing Function-Link Fuzzy Cerebellar Model Articulation Controller

An antilock braking system (ABS) is designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining an adequate ability to steer the vehicle. However, the performance of ABS is often degraded under harsh road conditions. In this paper, a self-organizing function-link fuzzy cerebellar model articulation controller (SOFFC) is proposed and is used as the uncertainty observer of the ABS. The self-organizing approach automatically generates and prunes the fuzzy rules for the SOFFC, without the need for preliminary knowledge. The learning algorithms not only extract the fuzzy rules for the SOFFC, but adjust the parameters of the SOFFC as well. A hybrid control system, composing a computational controller and a hyperbolic tangent compensator (HTC), is then proposed for the ABS. The computational controller, which contains an SOFFC uncertainty observer, forms the principal controller, and the HTC is used to compensate for the estimation uncertainty, in order to achieve ultimately bounded stability in the system. Finally, simulations are performed that demonstrate the effectiveness of the proposed hybrid control system in an ABS under various road conditions.

Vehicle Braking Performance Improvement By SVM Based Road Surface Detection

Advance information about the road surface a vehicle is going to encounter can improve the performance of Antilock Braking System (ABS). For e.g. the initial slip cycles caused by the ABS could be avoided, if it is already known that the vehicle is on a surface having a low coefficient of friction (μ). In this paper, an algorithm is developed that detects different road surfaces using streaming video acquired from a camera mounted on the hood of the vehicle. The road surfaces detected here are asphalt road, cement road, sandy road, rough asphalt road (asphalt road which is deteriorating), grassy road and rough road. The value of coefficient of friction (μ) is also given out with the detected surfaces to obtain additional information about the road surfaces. Split μ (a road having different μ conditions on the left and right side of the vehicle) and μ jump (different μ conditions on the front and rear of the vehicle) are also pre detected. One method was not sufficient to achieve the goals of this algorithm. Here several simple techniques like the Canny edge algorithm, intensity histogram, contours, Hough transform and image segmentation were employed and compared with the Support Vector Machine (SVM). To prevent misdetections, the road surface detection during high motion blur is prohibited.

Research on bench testing technology of automobile ABS: dynamic performance based on inertia simulation

One means of reducing the risk associated with the development of Anti-Lock Braking System (ABS) would be the ability to respond to the change of the tyre-road friction coefficient. In order to make accurate evaluation of the ABS performance, a possible solution is to research on bench testing technology. In this paper we put forward a simulation method of vehicle moment of inertia, in which the kinetic energy of the vehicle is replaced by the kinetic energy of the rotating flywheel, and the tyre-road friction coefficient is replaced by magnetic powder clutch. The amplitude of exciting current to the clutch has relationship with the friction coefficient, so as to provide a datum for testing the working status of ABS under various road conditions. The system can realise simulating test of single road surface, bisectional road surface and joint road surface. The validity of the road simulation method can be verified by the real-time data from the user interface.

Performance evaluation and test of anti-lock braking system hydraulic system

The model of anti-lock braking system (ABS) hy- draulic system is built including electromagnetic valve,brake line and wheel brake cylinder.By means of analyzing the characteristics of hydraulic system,a test bench is established to control and measure the behavioral data from the hydraulic system in real vehicles.Model parameters are calculated by regression analysis.Based on the model of ABS hydraulic sys- tem,ABS model is also built which includes vehicle model, electronic control unit model,etc.Important factors,which have critical effect on the performance of ABS,are discussed. Key evaluating parameters that can be used to evaluate the hydraulic system arc given based on the experimental data.The study can be used to guide the developing,calibration and matching of ABS for vehicles more effectively.

Self-learning fuzzy sliding-mode control for antilock braking systems

The antilock braking system (ABS) is designed to optimize braking effectiveness and maintain steerability; however, the ABS performance will be degraded in the case of severe road conditions. In this study, a self-learning fuzzy sliding-mode control (SLFSMC) design method is proposed for ABS. The SLFSMC ABS will modulate the brake torque for optimum braking. The SLFSMC system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is designed to mimic an ideal controller and the robust controller is designed to compensate for the approximation error between the ideal controller and the fuzzy controller. The tuning algorithms of the controller are derived in the Lyapunov sense; thus, the stability of the system can be guaranteed. Also, the derivation of the proposed SLFSMC ABS does not need to use a vehicle-braking model. Simulations are performed to demonstrate the effectiveness of the proposed SLFSMC ABS in adapting to changes for various road conditions.