神经网络模糊PID控制半主动悬架系统  

Research on Semi-active Suspension System Based on Neural Network Fuzzy PID Control

作　　者：王琳 王文博 钱爱文 WANG Lin;WANG Wenbo;QIAN Aiwen(School of Mechanical and Vehicle Engineering,Bengbu University,Bengbu 233030,China;School of Electrical Engineering and Automation,Luoyang Institute of Science and Technology,Luoyang 471023,China)

机构地区：[1]蚌埠学院机械与车辆工程学院,安徽蚌埠233030 [2]洛阳理工学院电气工程与自动化学院,河南洛阳471023

出　　处：《洛阳理工学院学报（自然科学版）》2022年第2期65-72,共8页Journal of Luoyang Institute of Science and Technology：Natural Science Edition

基　　金：安徽省科学研究项目(KJ2018A0568);蚌埠学院自然科学研究项目(2019ZR01).

摘　　要：针对半主动悬架系统随路面激励变化产生的振动与冲击,设计一种基于BP(Back Propagation)神经网络模糊PID(Proportional Integral Derivative)控制的半主动悬架控制器,以提升悬架系统的控制精度,改善汽车平顺性。以悬架系统的偏差与偏差变化率为输入参数,利用模糊逻辑规则对输入参数进行模糊化和归一化处理,处理结果作为BP神经网络的输入,通过神经网络在线调整加权系数,实现PID控制参数的优化。为了验证控制效果,对比了BP神经网络模糊PID控制半主动悬架系统、模糊PID控制半主动悬架系统和被动悬架系统的性能,BP神经网络模糊PID控制策略的半主动悬架系统具有更好的控制效果,能够显著改善不同路况下车辆悬架的性能。Aiming at the vibration and impact of semi-active suspension system with the change of road excitation,a controller based on BP(back propagation)neural network fuzzy PID(proportional integral derivative)control is designed and applied to semi-active suspension system to improve the control accuracy of suspension system and vehicle ride comfort.Taking the deviation and deviation change rate of the suspension system as the input parameters,the fuzzy logic rules are used to fuzzify and normalize the input parameters,and the results above as BP neural network input are used to adjust the weighting coefficient,so as to optimize the PID control parameters.In order to verify the control effect,the performances of semi-active suspension system controlled by BP neural network fuzzy PID and fuzzy PID and passive suspension are compared.The results show that the semi-active suspension system controlled by BP neural network fuzzy PID has better optimal control effect,which can significantly improve the performance of vehicle suspension under different road conditions.

关 键 词：半主动悬架 BP神经网络 模糊PID控制 1/4半主动悬架模型 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]