【摘要】 考虑汽车主动悬架的控制效果,应用汽车系统动力学理论建立七自由度整车悬架模型;采用电磁阀式减振器技术方案,对主动悬架控制系统进行了总体方案设计;在递归对角神经网络算法的基础上构建了主动悬架控制器,利用遗传算法进行神经网络权值训练。Simulink和d SPACE实时硬件在环联合仿真结果表明:在间歇颠簸路面激励作用下,对车身垂向加速度、轮胎动行程所进行的仿真分析,以及对车辆座椅进行的振动分析,都说明该算法对主动悬架具有较明显的控制效果,较好地提高了行驶平顺性和操纵稳定性。更多还原

【Abstract】 A 7-DOF( degree of freedom) vehicle suspension model is established based on the vehicle system dynamics theory in order to improve the control effect of vehicle active suspension. The overall scheme design of active suspension control system is carried out based on solenoid valve shock absorber technical scheme. The active suspension controller is constructed based on the DRNN( Diagonal Recurrent Neural Network) algorithm and a neural network is trained using genetic algorithm. The method has a self-study,internal feedback function. The results of real time Hardware-in-the-Loop simulation of Simulink and d SPACE show that the algorithm can obviously improve the control effect of vehicle active suspension by the simulation analysis of vehicle body vertical acceleration, tire dynamic displacement and the vibration analysis of vehicle seat under the effect of intermittent bumpy road excitation. DRNN algorithm is better to improve the riding comfort and handling stability.更多还原