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0.112976 mm、0.145829 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0.148038 mm、0.617804 g·s^(-2)。
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 0.112976 mm,0.145829 g·s^(-2) and 0.148038 mm,0.617804 g·s^(-2) at idle speed and high speed respectively.
作者
申玉瑞
华德正
王宁宁
刘晓帆
刘新华
SHEN Yurui;HUA Dezheng;WANG Ningning;LIU Xiaofan;LIU Xinhua(School of Mechatronic Engineering,China University of Mining and Technology,Xuzhou 221116,China;China Coal Education Association,Beijing 100713,China;Technology and Innovation Research Center of JiangYan EDZ,Taizhou 225500,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2021年第8期237-245,共9页
Journal of Chongqing University of Technology：Natural Science
基金
国家自然科学基金项目(51975568)
江苏省自然科学基金项目(BK20191341)
江苏高校优势学科建设工程项目(PAPD)
江苏省研究生科研与实践创新计划项目(KYCX20_2045)
中国矿业大学未来杰出人才助力计划项目(2020WLJCRCZL019)。
