农业装备与车辆工程2026，Vol.64Issue(7)：37-44,8.DOI:10.3969/j.issn.1673-3142.2026.07.006
基于BSO算法优化的悬架系统神经网络控制研究
Research on neural network control of suspension system optimized by BSO algorithm
摘要
Abstract
Vehicle passive suspensions have limited vibration reduction performance.Existing inerter-based suspension control strategies have insufficient adaptive ability.To solve these problems,this paper integrates an inerter into a variable-stiffness variable-damping suspension structure.It combines a 5-degree-of-freedom(5-DOF)human-seat model to establish a human-seat-suspension coupled dynamic model.This paper proposes an adaptive fuzzy neural PID control strategy optimized by beetle swarm optimization(BSO).A five-layer adaptive fuzzy neural network(AFNN)realizes full-process mapping of fuzzy inference.A hybrid RLS-BP learning algorithm completes online adaptive update of the network parameters.This paper takes the integral of time-weighted absolute error(ITAE)as the fitness function.It uses the BSO algorithm to achieve global optimization of the controller's core parameters.Simulation results verify the effectiveness of the proposed strategy.Under the Class-C random road condition,the RMS values of vertical acceleration of human head and pelvis are reduced by 55.76%and 27.38%respectively compared with the passive suspension,and the resonance peak in the 4～8 Hz frequency band sensitive to human body is effectively suppressed.Under the bump impact condition,the peak value of human head acceleration is controlled within 3 m/s2,and the vibration attenuation time is shortened to less than 2 s.It greatly improves the anti-vibration performance of the suspension under impact conditions,fully taps the vibration reduction potential of the inerter-based suspension,and significantly improves vehicle ride comfort.关键词
惯容器/变刚度变阻尼/人体-座椅/神经网络/天牛群优化算法Key words
inerter/variable stiffness variable damping/human body-seat/neural network/BSO algorithm分类
信息技术与安全科学引用本文复制引用
綦孝然,张艳龙..基于BSO算法优化的悬架系统神经网络控制研究[J].农业装备与车辆工程,2026,64(7):37-44,8.基金项目
甘肃省科技计划资助项目(21YF5WA060) （21YF5WA060）
