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浙江大学学报(工学版)  2017, Vol. 51 Issue (8): 1551-1558    DOI: 10.3785/j.issn.1008-973X.2017.08.010
土木与交通工程     
自传感磁流变阻尼器实时阻尼力跟踪控制
陈昭晖1, 倪一清2
1. 福州大学 土木工程学院, 福建 福州 350116;
2. 香港理工大学 土木及环境工程学系, 香港
Real-time damping-force tracking control of self-sensing magnetorheological dampers
CHEN Zhao-hui1, NI Yi-qing2
1. College of Civil Engineering, Fuzhou University, Fuzhou 350116, China;
2. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong
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摘要:

为了提高自传感磁流变阻尼器(SMRD)的控制性能,提出考虑SMRD逆向动力学的线性二次高斯(LQG)同位控制策略(i-LQG).采用贝叶斯非线性自回归(NARX)网络方法建立SMRD以控制导向的正向和逆向动力学模型,融入LQG控制回路补偿SMRD的滞回非线性,实现半主动阻尼力跟踪控制.开展试验比较i-LQG控制和基于Heaviside阶跃函数的LQG控制(H-LQG)下SMRD对控制力实时跟踪效果.结果表明,i-LQG控制下输出电压连续变化,改善了SMRD阻尼力实时跟踪性能,误差相比H-LQG控制减小50%;i-LQG控制下的结构系统阻尼比相比于H-LQG控制时提高11%,验证采用i-LQG控制策略可达到更高效的半主动结构控制性能.

Abstract:

In order to enhance the control performance of a self-sensing magnetorheological damper (SMRD), an inverse dynamics-based collocated linear-quadratic-Gaussian (LQG) control strategy (i-LQG) was proposed. Control-oriented forward and inverse dynamic models of the SMRD were developed by employing a Bayesian NARX (nonlinear autoregressive with exogenous inputs) network technique to represent its nonlinear dynamics. The dynamic models were further incorporated into the LQG control loop to compensate for the hysteretic nonlinearity of the SMRD and to implement semi-active damping-force tracking control. Experiments were conducted to compare the real-time force tracking performance when the SMRD was controlled by the i-LQG control and the Heaviside step function-based LQG (H-LQG) control, respectively. Results show that the i-LQG control commands continuously varying voltage to enhance the real-time SMRD damping-force tracking with a 50% reduction of the force tracking error beyond the H-LQG control. The structural damping with the i-LQG control is increased by 11% compared with that with the H-LQG control, which verifies that the proposed i-LQG control is able to realize more efficient semi-active structural control performance.

收稿日期: 2016-06-25 出版日期: 2017-08-16
CLC:  TB535  
基金资助:

国家自然科学基金资助项目(51608128);福建省自然科学基金资助项目(2016J05123);福建省教育厅科技项目(JA15098);福州大学校人才基金(XRC-1457).

作者简介: 陈昭晖(1982-),男,博士,助理研究员,从事结构振动控制等研究.ORCID:0000-0002-9727-8847.E-mail:zhchen@fzu.edu.cn
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引用本文:

陈昭晖, 倪一清. 自传感磁流变阻尼器实时阻尼力跟踪控制[J]. 浙江大学学报(工学版), 2017, 51(8): 1551-1558.

CHEN Zhao-hui, NI Yi-qing. Real-time damping-force tracking control of self-sensing magnetorheological dampers. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(8): 1551-1558.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2017.08.010        http://www.zjujournals.com/eng/CN/Y2017/V51/I8/1551

[1] LI H, HUO L. Advances in structural control in civil engineering in China[J]. Mathematical Problems in Engineering, 2010, 2010:936081.
[2] CASCIATI F, RODELLAR J, YILDIRIM U. Active and semi-active control of structures-theory and applications:A review of recent advances[J]. Journal of Intelligent Material Systems and Structures, 2012, 23(11):1181-1195.
[3] WEBER F, DISTL H. Amplitude and frequency independent cable damping of Sutong Bridge and Russky Bridge by magnetorheological dampers[J]. Structural Control and Health Monitoring, 2015, 22(2):237-254.
[4] DYKE S J, SPENCER B F Jr, SAIN M K, et al. Modeling and control of magnetorheological dampers for seismic response reduction[J]. Smart Materials and Structures, 1996, 5(5):565-575.
[5] MALANKA M, SAPISKI B, SNAMINA J. Experimental study of vibration control of a cable with an attached MR damper[J]. Journal of Theoretical and Applied Mechanics, 2007, 45(4):893-917.
[6] SEONG M S, CHOI S B, HAN Y M. Damping force control of a vehicle MR damper using a Preisach hysteretic compensator[J]. Smart Materials and Structures, 2009, 18(7):074008.
[7] WEBER F. Robust force tracking control scheme for MR dampers[J]. Structural Control and Health Monitoring, 2015, 22(12):1373-1395.
[8] EKKACHAI K, TUNGPIMOLRUT K, NILKHAMHANG I. Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network[J]. Smart Materials and Structures, 2013, 22(11):115030.
[9] 廖英英,刘永强,杨绍普,等.磁流变阻尼器逆向模型的建模、优化与仿真[J].振动、测试与诊断,2013,33(4):701-705. LIAO Ying-ying, LIU Yong-qiang, YANG Shao-pu, et al. Modeling, optimization and simulation for inverse model of magnetorheological damper[J]. Journal of Vibration, Measurement & Diagnosis, 2013, 33(4):701-705.
[10] CHEN Z H, NI Y Q, OR S W. Characterization and modeling of a self-sensing MR damper under harmonic loading[J]. Smart Structures and Systems, 2015, 15(4):1103-1120.
[11] NI Y Q, CHEN Z H, OR S W. Experimental identification of a self-sensing magnetorheological damper using soft computing[J]. Journal of Engineering Mechanics, 2015, 141(7):04015001.
[12] ASKARI M, MARKAZI A H D. A new evolving compact optimised Takagi-Sugeno fuzzy model and its application to nonlinear system identification[J]. International Journal of Systems Science, 2012, 43(4):776-785.
[13] 孙欣,丁建国,朱炜.基于优化模糊控制规则的磁流变阻尼减震结构地震反应分析[J].工程抗震与加固改造,2015,37(1):51-57. SUN X, DING Jian-guo, ZHU Wei. Seismic response control of the structure with smart mitigation system using magnetorheological fluid dampers based on optimizing fuzzy control rules[J]. Earthquake Resistant Engineering and Retrofitting, 2015, 37(1):51-57.
[14] 郑玲,周忠永.基于自适应神经模糊的磁流变阻尼器非参数化建模[J].振动与冲击,2011, 30(10):25-29. ZHENG Ling, ZHOU Zhong-yong. Non-parametric modeling for a magneto-rheological (MR) damper based on an adaptive neuro-fuzzy inference system[J]. Journal of Vibration and Shock, 2011, 30(10):25-29.
[15] ZONG L H, GONG X L, XUANG S H, et al. Semi-active H∞ control of high-speed railway vehicle suspension with magnetorheological dampers[J]. Vehicle System Dynamics, 2013, 51(5):600-626.
[16] HØGSBERG J, KRENK S. Energy dissipation control of magnetorheological damper[J]. Probabilistic Engineering Mechanics, 2008, 23(2/3):188-197.

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