Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (5): 13-19.doi: 10.6040/j.issn.1672-3961.0.2019.509

• Civil Engineering • Previous Articles     Next Articles

Fuzzy control of structure vibration mode based on BP neural network algorithm

Zhiwei WANG(),Nan GE*(),Chunwei LI   

  1. College of Architecture Engineering, North China University of Science and Technology, Tangshan 063009, Hebei, China
  • Received:2019-09-04 Online:2020-10-20 Published:2020-10-19
  • Contact: Nan GE E-mail:1912564765@qq.com;genanas@163.com

RichHTML

23

PDF (PC)

406

Abstract:

In order to control the seismic response of civil engineering structures more reasonably and conveniently, a fuzzy control algorithm based on BP neural network was proposed. The neural network was trained with the structural seismic dynamic response data to establish the structural analysis model, and the time-domain modal coordinates were taken as the controlled variables to reduce the order of the system, so that the number of fuzzy reasoning required to establish the modal fuzzy control rules was within the acceptable range, and the system energy minimum was taken as the control target to formulate the control rules. The fuzzy control numerical model of structural dynamic response was established to evaluate the damping effect of the proposed algorithm based on the calculated value of seismic dynamic response. The results showed that the trained BP neural network could accurately predict the seismic dynamic response of the structure and establish fuzzy control rules accordingly. Using mode fuzzy control only for the first mode of the structure could achieve satisfactory damping effect. When active mass driver(AMD) optimal control amplitude was used as the control domain of each floor, the damping effect of modal fuzzy control was different from it. A better damping effect could be obtained by increasing the control field.

Key words: neural network, state variables, fuzzy mode control, principle modes, inter-storey drift

CLC Number: 

  • TU352.1

Fig.1

Identification model structure of artificial BP neural network system"

Fig.2

Plan of 20-story building structure model"

Fig.3

The first three mode shapes"

Fig.4

Fuzzy control charts with different control forces"

Table 1

Maximum Inter-storey displacement and storey acceleration of seismic dynamic response"

楼层 模糊控制UA=3 模糊控制UA=7 无控制
层间位移/mm 层间位移减震效率/% 楼层加速度/(m·s-2) 楼层加速度减震效率/% 层间位移/mm 层间位移减震效率/% 楼层加速度/(m·s-2) 楼层加速度减震效率/% 层间位移/mm 楼层加速度/(m·s-2)
1 19.81 16.13 2.88 -10.34 12.15 48.59 2.17 16.85 23.62 2.61
2 19.65 15.80 5.55 -8.53 12.11 48.13 4.05 20.79 23.34 5.12
3 19.40 15.15 7.86 -6.35 12.04 47.34 5.76 22.09 22.86 7.39
4 19.14 14.10 9.49 -0.83 11.97 46.31 7.31 22.35 22.28 9.42
5 18.92 12.53 10.62 6.57 11.82 45.33 7.70 32.19 21.63 11.36
6 18.63 10.57 11.08 14.08 11.52 44.71 7.36 42.92 20.84 12.90
7 18.55 6.48 11.54 17.56 11.11 44.00 8.50 39.25 19.83 13.99
8 18.38 1.15 11.88 19.14 10.69 42.50 10.42 29.07 18.59 14.69
9 18.02 -5.21 12.25 23.70 10.28 40.00 11.66 27.40 17.13 16.06
10 17.42 -11.92 12.52 27.46 9.83 36.85 12.21 29.25 15.57 17.25
11 16.56 -17.77 12.54 29.64 9.40 33.15 12.15 31.84 14.06 17.83
12 15.43 -18.63 13.67 23.01 9.01 30.71 11.89 33.03 13.01 17.75
13 14.07 -15.46 15.12 11.71 8.60 29.44 11.53 32.65 12.18 17.12
14 12.52 -10.82 16.27 -0.61 8.07 28.61 10.78 33.36 11.30 16.17
15 10.92 -6.49 17.16 -11.72 7.37 28.13 9.75 36.51 10.26 15.36
16 9.38 -4.21 17.79 -14.14 6.51 27.68 8.45 45.75 9.00 15.58
17 7.77 -3.28 18.16 -11.65 5.46 27.39 8.43 48.15 7.53 16.26
18 6.03 -3.21 18.47 -3.18 4.26 27.14 9.64 46.14 5.84 17.90
19 4.12 -3.33 19.48 -1.44 2.92 26.91 11.58 39.70 3.99 19.20
20 2.09 -3.43 20.24 -1.38 1.48 26.73 12.73 36.24 2.02 19.96

Fig.5

Time history of structural seismic dynamic response"

Fig.6

Structural amplitudes for seismic dynamic responses"

Fig.7

Time history of storey control force"

1 欧进萍. 结构振动控制:主动,半主动,智能控制[M]. 北京: 北京科学出版社, 2003.
2 杨世浩, 瞿伟廉, 郑明燕. 用多种群遗传算法优化结构振动模糊控制器[J]. 武汉理工大学学报, 2004, 26 (10): 46- 48.
YANG Shihao , QU Weilian , ZHENG Mingyan . Optimization of structural vibration fuzzy controller by multi-population Genetic algorithm[J]. Journal of Wuhan University of Technology, 2004, 26 (10): 46- 48.
3 HUNT K J , SHARBARO D , ZHIKOWSKI R , et al. Neural networks for control systems: a survey[J]. Automatica, 1992, 28 (6): 1083- 1112.
4 周其节, 徐建闽. 神经网络控制系统的研究与展望[J]. 控制理论与应用, 1992, 9 (6): 569- 577.
ZHOU Qijie , XU Jianmin . Research and prospect of neural network control system[J]. Control Theory and Application, 1992, 9 (6): 569- 577.
5 阎石, 林皋, 黎海林. 人工神经网络在结构振动控制中应用[J]. 大连理工大学学报, 2000, 40 (9): 589- 592.
YAN Shi , LIN Gao , LI Hailin . The application of artificial neural network in structural vibration control[J]. Journal of Dalian University of Technology, 2000, 40 (9): 589- 592.
6 王隆杰, 毛宗源. 利用神经网络进行推理的模糊控制器[J]. 控制理论与应用, 1994, 11 (4): 508- 512.
WANG Longjie , MAO Zongyuan . Fuzzy controller using neural network for reasoning[J]. Control Theory and Application, 1994, 11 (4): 508- 512.
7 黄永安, 邓子辰. 基于瞬时最优控制神经网络的建筑结构主动控制研究[J]. 振动与冲击, 2005, (2): 5- 8.
HUANG Yongan , DENG Zichen . Research on active control of building structure based on instantaneous optimal control neural network[J]. Vibration and Shock, 2005, (2): 5- 8.
8 汪权, 韩强强, 王肖东, 等. 地震作用下高层建筑结构的分散神经网络振动控制研究[J]. 计算力学学报, 2019, 36 (1): 77- 82.
WANG Quan , HAN Qiangqiang , WANG Xiaodong , et al. Research on vibration control of high-rise building structures under earthquake action based on distributed neural network[J]. Journal of Computational Mechanics, 2019, 36 (1): 77- 82.
9 ZIZOUNI K , FALI L , SADEK Y , et al. Neural network control for earthquake structural vibration reduction using MRD[J]. Frontiers of Structural and Civil Engineering, 2019, 13 (5): 1171- 1182.
10 王耀南. 神经网络自适应模糊控制在温度控制系统中的应用[J]. 信息与控制, 1996, 25 (4): 245- 251.
WANG Yaonan . Application of neural network adaptive fuzzy control in temperature control system[J]. Information and Control, 1996, 25 (4): 245- 251.
11 张志涌, 杨祖樱. MATLAB教程R2010a[M]. 北京: 北京航空航天大学出版社, 2011.
12 赵婷婷, 谭军, 金春峰. 谈时程分析中地震波的选取[J]. 山西建筑, 2017, 43 (14): 41- 43.
ZHAO Tingting , TAN Jun , JIN Chunfeng . Discussion on selection of seismic wave in time history analysis[J]. Shanxi Architecture, 2017, 43 (14): 41- 43.
13 高建思, 王贵君. 基于三角形和高斯模糊化的Mamdani模糊系统表示[J]. 模糊系统与数学, 2018, 32 (2): 25- 31.
GAO Jiansi , WANG Guijun . Representation of Mamdani fuzzy system based on triangle and gaussian fuzzification[J]. Fuzzy Systems and Mathematics, 2018, 32 (2): 25- 31.
14 宁响亮.结构振动控制的多目标优化和智能模糊控制[D].哈尔滨:哈尔滨工业大学, 2010.
NING Xiangliang. Multi-objective optimization and intelligent fuzzy control for structural vibration control[D]. Harbin: Harbin University of Technology, 2010.
15 CLOUGH Way R , PENZIEN Joseph . Dynamics of structures[M]. London, United Kingdom: McGraw-Hill Inc, 1993.
16 葛楠, 苏幼坡, 王兴国, 等. 结构TMD控制及AMD最优控制减震效果计算研究[J]. 建筑科学, 2017, 33 (3): 7- 13.
GE Nan , SU Youpo , WANG Xingguo , et al. Structural TMD control and AMD optimal control damping effect calculation[J]. Building Science, 2017, 33 (3): 7- 13.
17 王刚, 欧进萍. 基于系统特征响应的结构振动模糊控制规则建立与仿真分析[J]. 振动工程学报, 2002, 15 (1): 82- 85.
WANG Gang , OU Jinping . The fuzzy control rules of structure vibration based on system characteristic response are established and simulated[J]. Journal of Vibration Engineering, 2002, 15 (1): 82- 85.
18 张微敬, 王学敏. 基于动态模糊神经网络的结构主动控制仿真分析[J]. 地震工程与工程振动, 2010, 30 (4): 132- 138.
ZHANG Weijing , WANG Xuemin . Simulation analysis of structural active control based on dynamic fuzzy neural network[J]. Seismic Engineering and Engineering Vibration, 2010, 30 (4): 132- 138.
19 胡成宝, 王云岗, 凌道盛. 瑞利阻尼物理本质及参数对动力响应的影响[J]. 浙江大学学报(工学版), 2017, 51 (6): 1284- 1290.
HU Chengbao , WANG Yungang , LING Daosheng . The influence of rayleigh damping physical essence and parameters on dynamic response[J]. Journal of Zhejiang University (Engineering Edition), 2017, 51 (6): 1284- 1290.
[1] SUN Donglei, WANG Yan, YU Yixiao, HAN Xueshan, YANG Ming, YAN Fangqing. Interval prediction of short-term regional photovoltaic power based on BP neural network [J]. Journal of Shandong University(Engineering Science), 2020, 50(5): 70-76.
[2] Guoyong CAI,Xinhao HE,Yangyang CHU. Visual sentiment analysis based on spatial attention mechanism and convolutional neural network [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 8-13.
[3] LIAO Nanxing, ZHOU Shibin, ZHANG Guopeng, CHENG Deqiang. Image caption generation method based on class activation mapping and attention mechanism [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 28-34.
[4] Yifei LI,Zunhua GUO. A Chirplet neural network for automatic target recognition [J]. Journal of Shandong University(Engineering Science), 2020, 50(3): 8-14.
[5] Baoming JIN,Guangyi LU,Wei WANG,Lunyue DU. Research on BP neural network rainfall runoff forecasting model based on elastic gradient descent algorithm [J]. Journal of Shandong University(Engineering Science), 2020, 50(3): 117-124.
[6] Shiqi SONG,Yan PIAO,Zexin JIANG. Vehicle classification and tracking for complex scenes based on improved YOLOv3 [J]. Journal of Shandong University(Engineering Science), 2020, 50(2): 27-33.
[7] Chunyang LI,Nan LI,Tao FENG,Zhuhe WANG,Jingkai MA. Abnormal sound detection of washing machines based on deep learning [J]. Journal of Shandong University(Engineering Science), 2020, 50(2): 108-117.
[8] Ningning CHEN,Jianwei ZHAO,Zhenghua ZHOU. Visual tracking algorithm based on verifying networks [J]. Journal of Shandong University(Engineering Science), 2020, 50(2): 17-26.
[9] Yanping CHEN,Li FENG,Yongbin QIN,Ruizhang HUANG. A syntactic element recognition method based on deep neural network [J]. Journal of Shandong University(Engineering Science), 2020, 50(2): 44-49.
[10] Xiaojie CAO,Xiaohua LI,Hui LIU. Construction expansion online for a class of nonaffine nonlinear large-scale systems [J]. Journal of Shandong University(Engineering Science), 2020, 50(1): 35-48.
[11] Guoyong CAI,Qiang LIN,Kaiqi REN. Cross-domain text sentiment classification based on domain-adversarialnetwork and BERT [J]. Journal of Shandong University(Engineering Science), 2020, 50(1): 1-7,20.
[12] Jucheng YANG,Shujie HAN,Lei MAO,Xiangzi DAI,Yarui CHEN. Review of capsule network [J]. Journal of Shandong University(Engineering Science), 2019, 49(6): 1-10.
[13] Diankun ZHENG,Tongle XU,Zhaojie YIN,Qingmin MENG. Prediction method of tailing dam groundwater levels based on improved PSO-BP neural network [J]. Journal of Shandong University(Engineering Science), 2019, 49(3): 108-113.
[14] Xiaoxiong HOU,Xinzheng XU,Jiong ZHU,Yanyan GUO. Computer aided diagnosis method for breast cancer based on AlexNet and ensemble classifiers [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 74-79.
[15] Yijiang HE,Junping DU,Feifei KOU,Meiyu LIANG,Wei WANG,Ang LUO. Images auto-encoding algorithm based on deep convolution neural network [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 61-66.
Viewed
Full text
431
HTML PDF
Just accepted Online first Issue Just accepted Online first Issue
0 0 23 0 0 408

  From Others local
  Times 30 401
  Rate 7% 93%

Abstract
1312
Just accepted Online first Issue
0 0 1312
  From Others local
  Times 1308 4
  Rate 100% 0%

Cited
Web of Science  Crossref   ScienceDirect  Search for Citations in Google Scholar >>
 
This page requires you have already subscribed to WoS.
  Shared   
  Discussed   
[1] WANG Su-yu,<\sup>,AI Xing<\sup>,ZHAO Jun<\sup>,LI Zuo-li<\sup>,LIU Zeng-wen<\sup> . Milling force prediction model for highspeed end milling 3Cr2Mo steel[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 1 -5 .
[2] ZHANG Yong-hua,WANG An-ling,LIU Fu-ping . The reflected phase angle of low frequent inhomogeneous[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 22 -25 .
[3] LI Kan . Empolder and implement of the embedded weld control system[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 37 -41 .
[4] SHI Lai-shun,WAN Zhong-yi . Synthesis and performance evaluation of a novel betaine-type asphalt emulsifier[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 112 -115 .
[5] CHEN Rui, LI Hongwei, TIAN Jing. The relationship between the number of magnetic poles and the bearing capacity of radial magnetic bearing[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 81 -85 .
[6] WANG Bo,WANG Ning-sheng . Automatic generation and combinatory optimization of disassembly sequence for mechanical-electric assembly[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 52 -57 .
[7] JI Tao,GAO Xu/sup>,SUN Tong-jing,XUE Yong-duan/sup>,XU Bing-yin/sup> . Characteristic analysis of fault generated traveling waves in 10 Kv automatic blocking and continuous power transmission lines[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 111 -116 .
[8] . [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 27 -32 .
[9] LIU Wen-liang, ZHU Wei-hong, CHEN Di, ZHANG Hong-quan. Detection and tracking of moving targets using the morphology match in radar images[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(3): 31 -36 .
[10] LIU Zhongguo,ZHANG Xiaojing,LIU Boqiang,LIU Changchun, . The development of ultrasonic characterization of the biological tissue elasticity[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(3): 34 -38 .
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd