The Control Strategy And Simulation Of Vehicle Adaptive Front-light System

The vehicle industry is at a stage of rapid development in our country, our livesbecome more convenient, fast and comfortable by vehicle, but at the same time, itbrings danger to us. There are lots of vehicle traffic accidents, and night trafficaccidents account for the larger proportion, and night traffic accidents are moreserious than day. The main causes of night traffic accidents are that corners existlighting blind zone and front illumination is insufficient when they are at high-speed,so the research of vehicle adaptive front-light system, which can automatically adjustfront-light illumination angle, improve the corners as well as front lighting, is veryimportant and actual significance to improve the night driving safety and reducetraffic accident rate.The vehicle adaptive front-light system which can automatically adjustfront-light illumination angle according to the signals of vehicle speed sensor andsteering wheel angle sensor, and controlled by the AFS controller is researched in thepaper. First, the relationship of vehicle speed, front wheel turning angle andfront-light adjustment angle were analyzed, and the front-light horizontal and verticaladjustment angle calculation formulas were established and modified according to therelevant provisions. Second, the control strategy based on the fuzzy logic controltheory and RBF neural network control theory were developed respectively, and thecorresponding vehicle AFS simulation models were established in the MATLAB/Simulink. Third, the speed and angle input signals of straight sections, small anglesection, curved section, and a combination sections were designed, the correspondingvehicle AFS simulation models were simulated on the given input signals and get thefront-light adjustment angle dynamic output. Finally, the simulation results werecompared and analyzed, and the results show that the vehicle AFS based on fuzzycontrol and RBF neural network control can adjust front-light angle automatically andfast response, small overshoot, but the vehicle AFS based on fuzzy controlaccommodation time is shorter than that of RBF neural network control, and the fuzzycontroller is better than RBF neural network controller.