【摘要】 为了提高磁流变发动机悬置隔振性能,提出了一种基于改进果蝇优化算法（IFOA）的PID半主动控制方法。首先,通过Elman神经网络建立了磁流变发动机悬置的正向、逆向模型,采集大量动态特性实验数据进行训练;然后,提出了一种基于云模型算法改进的果蝇优化算法,用于磁流变发动机悬置系统的PID控制器设计;最后,设计了磁流变悬置减振控制实验,检验所提出的基于IFOA的PID控制策略的有效性。实验结果显示:Elman神经网络逆模型的最低辨识精度为98.10%;低速工况下,IFOA-PID算法控制的发动机悬置系统的位移均方根值和加速度均方根值分别是0.112 976 mm、0.145 829 g·s^-2;高速工况下,其位移均方根值和加速度均方根值分别是0.148 038 mm、0.617 804 g·s^-2。更多还原

【Abstract】 In order to improve the vibration isolation performance of magnetorheological（ MR） Engine mounts,a PID semi-active control method based on the improved fruit fly optimization algorithm（ IFOA） is proposed. Firstly,the positive and reverse models of MR engine mount combined with Elman neural network are established by using Elman neural network, and a large amount of experimental data of dynamic characteristics are collected for training. Then an IFOA which improved by cloud model algorithm is proposed and applied to the PID controller design of the MR mount.Finally,the vibration control experiments were carried out and the curves of control effect of FOA,PSO-FOA and IFOA were obtained. As a result,the minimum approximation accuracy of the Elman neural network inverse model is 98. 10%,and the RMS of vibration displacement and vibration acceleration of MR engine mount based on IFOA-PID controller are 0. 112 976 mm,0. 145 829 g·s^-2 and 0. 148 038 mm,0. 617 804 g·s^-2 at idle speed and high speed respectively.更多还原