粒子群算法优化的车辆悬架座椅模糊PID控制  

Fuzzy PID Control of Vehicle Suspension Seat Optimized by Particle Swarm Algorithm

作　　者：兰靛靛 甘达[1] 林鸿森 林祖胜 LAN Diandian;GAN Da;LIN Hongsen;LIN Zusheng(School of Mechanical and Automotive Engineering,Xiamen University of Technology,Xiamen 361024,China;Fujian Key Laboratory of Bus Advanced Design and Manufacture,Xiamen University of Technology,Xiamen 361024,China;Advanced Electric Drive Technology Innovation Branch,Xiamen National Innovation Center,Xiamen 361006,China)

机构地区：[1]厦门理工学院机械与汽车工程学院,福建厦门361024 [2]厦门理工学院福建省客车先进设计制造重点实验室,福建厦门361024 [3]厦门国创中心先进电驱动技术创新中心,福建厦门361006

出　　处：《华侨大学学报(自然科学版)》2025年第1期23-29,共7页Journal of Huaqiao University(Natural Science)

基　　金：福建省自然科学基金资助项目(2021J011199)。

摘　　要：针对车辆悬架座椅的振动问题,基于ADAMS/View和MATLAB/Simulink平台建立三自由度1/4车辆主动悬架座椅系统模型和路面模型,提出一种运用粒子群算法优化模糊PID的控制方法。该方法融合标准粒子群算法与模糊PID算法,通过粒子群算法对模糊PID控制中的量化因子、比例因子和模糊规则参数进行优化,解决模糊PID控制参数的选取存在经验性和主观性的问题。仿真结果表明:在不同的车速下,相较于模糊PID控制,粒子群优化模糊PID控制的座椅加速度下降16.5%以上,相较于被动悬架座椅,粒子群优化模糊PID控制的座椅加速度下降48.0%以上,减振效果改善明显。Aiming at addressing the vibration problem of vehicle suspension seat,a three-degree-of-freedom 1/4 vehicle active suspension seat system model and a road profile model were established based on ADAMS/View and MATLAB/Simulink platforms,and a control method using particle swarm algorithm to optimize fuzzy PID was proposed.This method integrates the standard particle swarm algorithm with the fuzzy PID algorithm,optimizing the quantization factor,scale factor and fuzzy rule parameters in the fuzzy PID control through the particle swarm algorithm,to solve the problem of empirical and subjective selection of the fuzzy PID control parameters.The simulation results indicate that,under different vehicle speeds,the seat acceleration using particle swarm optimized fuzzy PID control is reduced by more than 16.5%compared to fuzzy PID control,and by over 48.0%compared to passive suspension seats,thereby significantly enhancing the damping effect.

关 键 词：悬架座椅 粒子群算法 模糊PID控制 硬件在环仿真试验 

分 类 号：U461.4[机械工程—车辆工程]