Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (5): 1608-1616.doi: 10.13229/j.cnki.jdxbgxb20190469

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Damping optimization of heavy⁃loaded anti⁃vibration platform based on genetic algorithm and particle swarm algorithm

Fang-wu MA(),Li HAN,Liang WU(),Jin-hang LI,Long-fan YANG   

  1. State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
  • Received:2019-05-17 Online:2020-09-01 Published:2020-09-16
  • Contact: Liang WU E-mail:mikema@jlu.edu.cn;astdwxg@jlu.edu.cn

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Abstract:

Heavy-loaded Anti-Vibration Platform (AVP) is mainly used to carry high-precision instruments and equipments to isolate vibration and impact caused by uneven complex road surface, thus, providing a stable working environment. In this paper, taking the 6 DOF series AVP as the research object, the parameters of the AVP damping system are optimized by the usage of the fast elitist multi-objective genetic algorithm (NSGA_II) and multi-objective particle swarm optimization algorithm (MOPSO) to significantly improve the anti-vibration performance of AVP. The damping and stiffness parameters of the damping system are optimized by NSGA-II and MOPSO under various road surface analog signals. The optimization results show that the anti-vibration rate of the two optimization targets of vertical acceleration and vertical displacement of upper platform under different excitation can be up to 56.21%. In addition, NSGA-II is applied to optimize the damper support angle parameters of the AVP, which increases the anti-vibration rate of the two optimization targets by 7.00%. The sine excitation is verified by experiments, and the error between experiment and simulation is as small as 8.30%, thus verifying the effectiveness of optimization parameters.

Key words: anti-vibration platform, virtual prototyping, multi-objective optimization, anti-vibration rate, multi-objective particle swarm optimization algorithm

CLC Number: 

  • U463.99

Fig.1

3D mechanical model of AVP"

Fig.2

Virtual prototype model and mathematical model concept map"

Fig.3

Contrast graph of two models"

Fig.4

Sine excitation"

Fig.5

Impulse excitation"

Fig.6

Step excitation"

Fig.7

Algorithm of NSGA-II and MOPSO"

Table 1

Parameters of AVP system"

参数数值
簧上质量/kg650
簧下质量/kg300
阻尼/(N·s·m-1)1 500
空气弹簧刚度/(N·m-1)16 800
钢索弹簧刚度/(N·m-1)200 000

Table 2

Initial values and limits of parameter variables"

参数初值上限下限
DV90.4880.4390.537
DV100.9380.8441.032
DV110.4860.4370.535
DV121.0450.9411.150
DV130.4880.4390.537
DV140.9380.8441.032
DV151.0360.9321.140
DV160.4860.4370.535

Table 3

Variable values before and after optimization"

参数NSGA-II优化前
DV90.5000.480
DV101.0160.938
DV110.3920.486
DV121.1001.045
DV130.3890.488
DV141.0330.938
DV151.1281.036
DV160.3940.486

Fig.8

Comparison of vertical acceleration response before and after optimization"

Fig.9

Comparison of vertical displacement response before and after optimization"

Table 4

Parameter values before and after optimization of stiffness and damping parameters under sine excitation"

变量NSGA-IIMOPSO优化前
S11 1301 0001 500
S21 0441 0261 500
S31 0602 0001 500
S41 2471 0001 500
S51 3641 0001 500
S61 3821 0001 500
S71 0861 0001 500
S81 0781 0001 500
S910 05010 00016 800
S1010 01710 47716 800
S1110 02810 00016 800
S1210 03510 00016 800

Table 5

Parameter values before and after optimization of stiffness and damping parameters under impulse excitation"

变量NSGA-IIMOPSO优化前
S11 3521 3601 500
S21 1321 0851 500
S31 5741 0001 500
S41 1101 0001 500
S51 2541 0001 500
S61 4821 0001 500
S71 2431 0861 500
S81 1652 0301 500
S912 20510 16116 800
S1010 00211 15616 800
S1113 43013 48216 800
S1214 20210 59716 800

Table 6

Parameter values before and after optimization of stiffness and damping parameters under step excitation"

变量NSGA-IIMOPSO优化前
S11 0761 0001 500
S22 1401 0001 500
S31 8253 0001 500
S41 0431 0461 500
S51 7001 8101 500
S61 1401 0001 500
S71 1401 8601 500
S81 2801 0001 500
S911 70012 40016 800
S1014 00010 20016 800
S1111 00010 00016 800
S1210 40011 90016 800

Fig.10

Response comparison chart"

Table 7

Comparison of vertical acceleration before and after optimization"

项目优化前加速度/（m·s-2）NSGA-IIMOPSO
加速度/（m·s-2）隔振率/%加速度/（m·s-2）隔振率/%
正弦1.4180.80243.440.80143.51
脉冲0.8380.70515.870.66121.12
阶跃0.8380.73811.930.73811.93

Table 8

Comparison of vertical displacement before and after optimization"

项目优化前 位移/mmNSGA-IIMOPSO
位移/（m·s-2）隔振率/%位移/（m·s-2）隔振率/%
正弦8.453.7056.213.8055.00
脉冲4.103.1024.402.7034.15
阶跃8.608.006.987.7010.47

Fig.11

Experimental site and sensors layout"

Fig.12

Comparison of experimental and NSGA-II optimized response"

Fig.13

Time domain sweep experiment"

Fig.14

Frequency domain sweep"

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