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改进粒子群算法在LQR半主动悬架的应用

王习昌 鲍东杰

王习昌,鲍东杰. 改进粒子群算法在LQR半主动悬架的应用[J]. 机械科学与技术,2023,42(3):468-474 doi: 10.13433/j.cnki.1003-8728.20200631
引用本文: 王习昌,鲍东杰. 改进粒子群算法在LQR半主动悬架的应用[J]. 机械科学与技术,2023,42(3):468-474 doi: 10.13433/j.cnki.1003-8728.20200631
WANG Xichang, BAO Dongjie. Application of Improved Particle Swarm Algorithm in Vehicle LQR Semi-active Suspension[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(3): 468-474. doi: 10.13433/j.cnki.1003-8728.20200631
Citation: WANG Xichang, BAO Dongjie. Application of Improved Particle Swarm Algorithm in Vehicle LQR Semi-active Suspension[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(3): 468-474. doi: 10.13433/j.cnki.1003-8728.20200631

改进粒子群算法在LQR半主动悬架的应用

doi: 10.13433/j.cnki.1003-8728.20200631
详细信息
    作者简介:

    王习昌(1998−),硕士研究生,研究方向为汽车NVH、计算流体动力学,1577175000@qq.com

  • 中图分类号: TG156

Application of Improved Particle Swarm Algorithm in Vehicle LQR Semi-active Suspension

  • 摘要: 针对车辆半主动悬架LQR控制中Q矩阵和R矩阵往往由经验取值的问题,提出一种基于改进粒子群算法的LQR控制方法。该算法采用随机惯性权重代替了传统粒子群算法的固定惯性权重,提高了求解精度和效率,得到了更加具有适应性的LQR控制矩阵系数。为验证此方法的有效性,基于天棚阻尼模型建立1/4车被动悬架模型和半主动悬架模型,利用线性二次最优控制建立LQR控制器,并利用优化算法得到新的控制矩阵。通过仿真对比被动悬架、LQR控制的LQR半主动悬架、改进粒子群算法优化后的优化LQR悬架的各项性能参数,发现优化LQR悬架在悬架动挠度没有受到影响的前提下,使车辆的垂向加速度和轮胎动载荷得到有效降低,提高了车辆的行驶平顺性和操纵安全性。
  • 图  1  路面位移的输入模型

    图  2  C级路面下车速为80 km/h的路面位移

    图  3  半主动悬架模型

    图  4  算法流程图

    图  5  基于四分之一车二自由度半主动悬架模型

    图  6  被动悬架和LQR半主动悬架时域响应对比( C 级路面,80 km/h )

    图  7  LQR半主动悬架和优化LQR悬架时域响应对比( C 级路面,80 km/h )

    表  1  四分之一车仿真参数

    模型参数数值
    簧载质量ms380 kg
    非簧载质量mu40 kg
    弹簧刚度ks20 000 N/m
    轮胎刚度kt250 000 N/m
    阻尼器阻尼系数Cs1 500 N·s/m
    下截止频率f00.1 Hz
    下载: 导出CSV

    表  2  各项评价指标的均方根值

    参数被动悬架LQR半主动悬架优化LQR悬架
    车身加速度0.999 0690.774 8140.707 692
    悬架动挠度8.88376 × 10−37.49789 × 10−36.81384 × 10−3
    轮胎动载荷737.6601.9544.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-15
  • 刊出日期:  2023-03-25

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