Intelligent Braking System Using Fuzzy Logic and Sliding Mode Controller

In this paper, an antilock–antiskid braking system controller, which has been designed for stability enhancement of vehicles during braking and turning, is presented. Using available signals, a novel structure is proposed for vehicle stability improvement for critical driving conditions such as braking on slippery or μ-split road surfaces. In conventional vehicles, undesired lane changes may occur due to equal dispatch of braking torques to all wheels simultaneously. Also, intensive pressure on brake pedal can bring about wheel lockup which results in vehicle instability and undesired lane changes. Antilock braking system (ABS) along with Antiskid braking system (ASBS) can serve as a driver-assistance system in vehicle path correction facing critical driving conditions during braking and turning round on different road surfaces. For these purposes, at first, a trained Neuro-Fuzzy estimator is used for vehicle path prediction according to vehicle’s speed, applied steering angle and their changes. Then, slip of each wheel will be controlled by an antilock fuzzy controller which has been designed for each wheel. Also, a sliding mode controller has been designed so as to control the yaw angle considering the yaw error, where the yaw error is resulted from the difference of the Neuro-Fuzzy estimator’s output and actual yaw angle. Then, the difference of the left and the right wheels’ braking torques are used by the sliding mode controller in order to reduce the yaw error. Considering a model of three-degreeof-freedom for chassis and one-degree-of-freedom Dugoff’s tire model for each wheel, a series of Matlab/Simulink simulation results will be presented to validate the effectiveness of the proposed controller. Finally, a comparison will be made between the proposed method and one of the recent control systems which shows the superiority of its performance.