The work aims to improve the driving performance of the whole vehicle so that the vehicle can autonomic condi-tioning to adapt to a variety of different road surfaces to achieve a good buffer effect.The composition and working principle and relationship between the damping force and stiffness of hydro-pneumatic spring and the displacement of vehicle body was deduced in detail.According to the principle of RBF neural network control,a RBF-PID controller was designed,and the PID parameters were dynamically adjusted by the self-learning habit of neural network,so that the vibration of the whole body at-tenuated and reached a stable state quickly.A simulation model was established based on Matlab/Simulink platform.With the input of Class B and Class C uneven road surface,the three performances of tire dynamic load,body centroids acceleration and dynamic deflection of oil-gas suspension were simulated and analyzed.Compared with the ordinary PID control,the RMSE of three performance of the body under RBF-PID control was reduced by 26%,54%and 0.3%respectively.RBF-PID control can overcome the impact of the environment,realize the reliable control of the characteristics of the oil-gas spring,and improve the ride comfort and stability of the vehicle.
关键词
油气弹簧/自主调节/动态整定/RBF-PID控制器/快速稳定/可靠控制
Key words
hydro-pneumatic spring/autonomic conditioning/dynamically adjusted/RBF-PID controller/reach stable state quickly/reliable control
维普
万方数据