基于改进海鸥算法的磁流变减振器模型辨识

doi:10.13229/j.cnki.jdxbgxb20200887

Abstract

Abstract:

In this paper， a method is presented to identify parameters of Bouc-Wen hysteresis model of magnetorheological damper. Based on the new meta-heuristic algorithm—Seagull optimization algorithm， the model parameters describing the nonlinear mechanical properties of the magnetorheological damper were fitted and identified. The opposition-based learning model based on the principle of refraction was introduced to improve the problem of falling into local optimal of the seagull optimization algorithm. The calculation results of the parameter identification method are accurate， simple and robust. The relationship between the model parameters and the control current is established by fitting and calculating the test data of the magnetorheological damper under different operating conditions. A general model of the magnetorheological damper including the control current is established and provided an accurate mechanical model for semi-active seat suspension control system.

Key words: vehicle engineering, semi-active suspension, magnetorheological damper, parameter identification, meta-heuristic intelligent algorithm

CLC Number:

U461.4

Wen-ku SHI,Shu-guang ZHANG,You-kun ZHANG,Zhi-yong CHEN,Yi-fei JIANG,Bin-bin LIN. Parameter identification of magnetorheological damper model with modified seagull optimization algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 764-772.

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References 20

1	Zong L H, Gong X L, Xuan S H, et al. Semi-active H infinity control of high-speed railway vehicle suspension with magnetorheological dampers[J]. Vehicle System Dynamics, 2013, 51(5): 600-626.
2	Mai V N, Yoon D S, Choi S B, et al. Explicit model predictive control of semi-active suspension systems with magneto-rheological dampers subject to input constraints[J]. Journal of Intelligent Material Systems and Structures, 2020, 31(10): 1157-1170.
3	Du X M, Yu M, Fu J, et al. Experimental study on shock control of a vehicle semi-active suspension with magneto-rheological damper[J]. Smart Materials and Structures, 2020, 29(7): No.074002.
4	Phu D X, Shin D K, Choi S B. Design of a new adaptive fuzzy controller and its application to vibration control of a vehicle seat installed with an MR damper[J]. Smart Materials and Structures, 2015, 24(8): No. 085012.
5	Bai X X, Chen P, Qian L J. Principle and validation of modified hysteretic models for magnetorheological dampers[J]. Smart Materials and Structures, 2015, 24(8): No. 085014.
6	Wang W X, Hua X G, Wang X Y, et al. Mechanical behavior of magnetorheological dampers after long-term operation in a cable vibration control system[J]. Structural Control and Health Monitoring, 2019, 26(1): No.e2280.
7	Zhang C, Wang L X. Dynamic models for magnetorheological dampers and its feedback linearization[J]. Advanced Materials Research, 2009, 79-82: 1205-1208.
8	左曙光, 毛钰, 吴旭东, 等. 磁流变减振器高频硬化特性建模及优化[J]. 振动与冲击, 2016, 35(10): 120-125, 150.
	Zuo Shu-guang, Mao Yu, Wu Xu-dong, et al. Modelling and optimization of high frequency hardening characteristics of magneto rheological damper [J]. Journal of Vibration and Shock, 2016, 35(10): 120-125, 150.
9	彭虎, 张进秋, 刘义乐, 等. 基于改进双Sigmoid模型的磁流变减振器力学建模研究[J].振动与冲击, 2019, 38(15): 216-222.
	Peng Hu, Zhang Jin-qiu, Liu Yi-le,et al. MR damper's modeling based on improved dual-sigmoid model [J]. Journal of Vibration and Shock, 2019, 38(15): 216-222.
10	薛兵, 杜永昌, 刘源, 等. 基于机理的磁流变减震器滞回特性魔术公式模型[J]. 振动工程学报, 2017, 30(5): 774-780.
	Xue Bing, Du Yong-chang, Liu Yuan, et al. Magic formula model of hysteresis characteristics of magnetorheological damper based on mechanism[J]. Journal of Vibration and Shock, 2017, 30(5): 774-780.
11	Charalampakis A E, Koumousis V K. Identification of bouc–wen hysteretic systems by a hybrid evolutionary algorithm[J]. Journal of Sound and Vibration, 2008, 314(3-5): 571-585.
12	Kwok N M, Ha Q P, Nguyen T H, et al. A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization[J]. Sensors and Actuators A: Physical, 2006, 132(2): 441-451.
13	郑玲, 牛伯瑶, 李以农, 等. 基于遗传算法的汽车磁流变减振器多目标优化[J]. 汽车工程, 2016, 38(7): 871-877.
	Zheng Ling, Niu Bo-yao, Li Yi-nong, et al. Multi-objective optimization of vehicle MR damper based on genetic algorithm[J]. Automotive Engineering, 2016, 38(7): 871-877.
14	刘永强, 杨绍普, 廖英英. 一种磁流变减震器模型参数识别新方法[J]. 机械工程学报, 2018, 54(6): 62-68.
	Liu Yong-qiang, Yang Shao-pu, Liao Ying-ying. A new method of parameters identification for magnetorheological damper model[J]. Journal of Mechanical Engineering, 2018, 54(6): 62-68.
15	Zeinali M, Mazlan S A, Fatah A Y A, et al. A phenomenological dynamic model of a magnetorheological damper using a neuro-fuzzy system[J]. Smart Materials and Structures, 2013, 22(12): No. 125013.
16	王戡, 郑玲, 刘非. 基于广义回归神经网络的磁流变减振器模型辨识[J]. 汽车工程, 2013, 35(7): 619-623, 634.
	Wang Kan, Zheng Ling, Liu Fei. Model identification of mr damper based on generalized regression neural network[J]. Automotive Engineering, 2013, 35(7): 619-623, 634.
17	Spencer B F, Dyke S J. Phenomenological model for magnetorheological dampers[J]. Journal of Engineering Mechanics, 1997, 123(3): 230-237.
18	Dhiman G, Kumar V. Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems[J]. Knowledge-Based Systems, 2019, 165: 169-196.
19	Cao Y, Li Y Q, Zhang G, et al. Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm[J]. Energy Reports, 2019, 5: 1616-1625.
20	喻飞,李元香,魏波, 等.透镜成像反学习策略在粒子群算法中的应用[J]. 电子学报, 2014, 42(2): 230-235.
	Yu Fei, Li Yuan-xiang, Wei Bo, et al. The application of a novel obl based on lens imaging principle in PSO[J]. Acta Electronica Sinica, 2014, 42(2): 230-235.

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频率/Hz	幅值/mm
频率/Hz	5	10	15	20
0.5	√	√	√	√
1	√	√	√	√
1.5	√	√	√	√
2	√	√	√	√
2.5	√	√	√	√
3	√	√	√	√
3.5	√	√	√	√
4	√	√	√	√
4.5	√	√	√	-
5	√	√	√	-
6	√	√	-	-
7	√	√	-	-
9	√	-	-	-

参数	名称	数值
M_s/kg	座椅重量	67
k_s/（N·m^-1）	悬架刚度	78 000
c_s/（N·m·s^-1）	被动减振器阻尼系数	942
F_d	磁流变减振器阻尼力	-
x_s	座椅上板位移	-
x_b	激励位移	-

Parameter identification of magnetorheological damper model with modified seagull optimization algorithm

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