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LIU Shun-an, HU Qing-yu, GAO Chun-fu, YU Xian-li, YAO Yong-ming, CHEN Yan-li. Application of IPSO-BP Network Algorithm in Semi-active Suspension Control[J]. Journal of Beijing University of Technology, 2011, 37(9): 1281-1286. DOI: 10.3969/j.issn.0254-0037.2011.09.001
Citation: LIU Shun-an, HU Qing-yu, GAO Chun-fu, YU Xian-li, YAO Yong-ming, CHEN Yan-li. Application of IPSO-BP Network Algorithm in Semi-active Suspension Control[J]. Journal of Beijing University of Technology, 2011, 37(9): 1281-1286. DOI: 10.3969/j.issn.0254-0037.2011.09.001
shu

Application of IPSO-BP Network Algorithm in Semi-active Suspension Control

  • 1. Mechanical Engineering Institute, Jilin University, Changchun 130022, China;

  • 2. Institute of Mechanical Device & Measurement and Control, Zhejiang Normal University, Jinhua 321004, Zhejiang, China

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  • Received Date: June 08, 2009
  • Available Online: November 18, 2022
    Graphical Abstract
    • Abstract
  • To improve the performance of semi-active suspension,the paper proposes an IPSO-BP algorithm as an adaptive semi-active suspension control algorithm.The IPSO algorithm improves the standard PSO algorithm to perfect the convergence rate and the capability of global convergence and is used for the BP neural network learning algorithm for adaptive semi-active suspension control.Adaptive controller has a two-unit structure neural network.One is an input controller,which adjusts the value of semi-active suspension damping in accordance with the road input;and the other is a semi-active suspension identifier which is used for online identification.Through the adaptive control test of the semi-active suspension based on the semi-active suspension controller,results show that the controller based on the IPSO-BP algorithm obviously improves the comfort and ride quality of the car.The vertical acceleration of the car body reduces 21.73% compared with the PSO-BP semi-active suspension.This algorithm improves the vehicle suspension performance.
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    • References (11)
  • [1]
    张宝琳.汽车主动悬架系统的最优跟踪控制[J].系统仿真学报,2009,21(2):551-553.ZHANG Bao-lin.Optimal tracking control for vehicle active suspension systems[J].Journal of System Simulation,2009,21(2):551-553.(in Chinese)
    [2]
    LEVESLEY M C,RAMLI R,STEMBRIDQE N,et al.Multi-body co-simulation of semi-active suspension systems[J].Journal of Multi-body Dynamics,2007,221(1):99-115.
    [3]
    GIORGETTI N,BEMPORAD A,TSENG H E,et al.Hybrid model predictive control application towards optimal semi-activesuspension[J].International Journal of Control,2006,79(5):521-533.
    [4]
    宋慧新,李翠芬.基于频率分析的半主动悬架模糊控制方法研究[J].汽车工程,2008,30(6):518-522.SONG Hui-xin,LI Cui-fen.A study on the fuzzy control of semi-active suspension based on frequency analyses[J].Automotive Engineering,2008,30(6):518-522.(in Chinese)
    [5]
    管继富,武云鹏,黄华,等.基于统计优化的车辆半主动悬架自适应控制[J].计算机测量与控制,2008,16(2):188-190.GUAN Ji-fu,WU Yun-peng,HUANG Hua,et al.Adaptive control based on statistical optimization for semi-active suspensionvehicle[J].Computer Measurement&Control,2008,16(2):188-190.(in Chinese)
    [6]
    张孝祖,乐巍,陈龙.阻尼模糊控制在车辆半主动悬架中的应用[J].农业机械学报,2004,35(2):5-8.ZHANG Xiao-zu,YUE Wei,CHEN Long.Application of fuzzy control on damping coefficient in vehicle semi-activesuspension[J].Transactions of the Chinese Society for Agricultural Machinery,2004,35(2):5-8.(in Chinese)
    [7]
    姜海明,谢康,王亚非.按概率突跳的改进微粒群优化算法[J].吉林大学学报:工学版,2007,37(1):141-145.JIANG Hai-ming,XIE Kang,WANG Ya-fei.Modified particle swarm optimization via probabilistic leap[J].Journal of JilinUniversity:Engineering and Technology Edition,2007,37(1):141-145.(in Chinese)
    [8]
    袁曾任.人工神经元网络及其应用[M].北京:清华大学出版社,1996.
    [9]
    KENNEDY J,EBERHART R.Particle swarm optimization[C]∥Proc IEEE International Conference on Neural Networks.Piscataway NJ:IEEE Service Center,1995:1942-1948.
    [10]
    崔长彩,黄富贵,张认成,等.粒子群优化算法及其在圆柱度误差评定中的应用[J].光学精密工程,2006,14(2):256-260.CUI Chang-cai,HUANG Fu-gui,ZHANG Ren-cheng,et al.Research on cylindricity evaluation based on the particle swarmoptimization(PSO)[J].Optics and Precision Engineering,2006,14(2):256-260.(in Chinese)
    [11]
    庄继德,陈善华,张宝生.可切换半主动悬架的一种自适应控制策略[J].中国公路学报,1998,11(3):103-109.ZHUANG Ji-de,CHEN Shan-hua,ZHANG Bao-sheng.An adaptive control law for switch able semi active suspension[J].China Journal of Highway and Transport,1998,11(3):103-109.(in Chinese)
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