Research Of Vehicle Anti-lock Braking System Based On Slip-ratio

Vehicle anti-lock braking system(ABS) is one of the most successful active safety devices, which is of great significant in preventing and reducing the traffic accidents. In the process of vehicle braking, it would be very easy to lead to the sideslip and even the failure of steering system. The ABS will avoid locking the wheels by continuously adjusting the vehicle's braking pressure. The vehicle's braking pressure is adjusted according to changes in the rate of slip in the braking process.First, the development history, present research situation and the future development trend of the ABS are introduced. Then, the principle of ABS is depicted by the research of the relationship between ground braking force, the brake force and the surface adhesion. Last, the structure and arrangement of ABS are introduced.According to the wheel braking force and the Newton's law, the dynamics model of vehicle, tire model and brake model are established. And the simulation model of ABS is established by MATLAB/Simulink. The design and analysis are done focused on the controller which has the characteristics of strong nonlinear. Because of the fuzzy control lacking of ability of online learning, adaptive ability, a fuzzy neural network controller is designed by combining t he artificial neural network and fuzzy control theory. The control effect is verified in the simulation experiment.Aiming at the disadvantages such as slow convergence speed and long training time of fuzzy neural network control algorithm, the an t colony algorithm is introduced to optimize the initial value of BP neural network in fuzzy neural network control algorithm and to improve the long convergence time of traditional BP algorithm. At the same time, aiming at the problem of premature convergence and insufficient ability of local optimization, the pheromone update strategy, pheromone range and pheromone volatilization coefficient of the standard ant colony algorithm is improved. The simulation results show that compared with the standard ant colony optimization algorithm, the optimize fuzzy neural network control of ABS based on improved ant colony algorithm, the improved algorithm has the performance of small sliding rate overshoot, fast convergence rate and shorter braking distance.Finally, the speed sensor circuit of the wheel, electromagnetic valve drive circuit and fault diagnosis circuit are designed with MSP430 single chip microcomputer. the main program of the PID control, part of the program of anti-lock control and fault diagnosis and ABS electronic control unit design are completed.