Article(id=1228634261973037619, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228634261138374834, articleNumber=null, orderNo=null, doi=10.16385/j.cnki.issn.1004-4523.2024.07.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1657296000000, receivedDateStr=2022-07-09, revisedDate=1663689600000, revisedDateStr=2022-09-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1770858834975, onlineDateStr=2026-02-12, pubDate=1722096000000, pubDateStr=2024-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1770858834975, onlineIssueDateStr=2026-02-12, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1770858834975, creator=13701087609, updateTime=1770858834975, updator=13701087609, issue=Issue{id=1228634261138374834, tenantId=1146029695717560320, journalId=1225147924628267009, year='2024', volume='37', issue='7', pageStart='1089', pageEnd='1268', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1770858834775, creator=13701087609, updateTime=1770859016311, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1228635022622654927, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228634261138374834, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1228635022622654928, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228634261138374834, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1126, endPage=1138, ext={EN=ArticleExt(id=1228634262291804725, articleId=1228634261973037619, tenantId=1146029695717560320, journalId=1225147924628267009, language=EN, title=Active control system of high tower based on displacement observer inside tower and low-dimension controller in equilibrium space, columnId=null, journalTitle=Journal of Vibration Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The vibration of high-rise tower structure requires active control. However,the problem of direct displacement observation and high-efficient low-dimension control strategy under single point needs to be solved. In this paper,a set of real-time continuous observation method of displacement from the inside of the tower is established by using modern video metrics technology,which provides the most direct displacement observer for the active control of the high tower. It avoids the tedious calculation process of building an acceleration observer,and then combining with the assumed external load to calculate the displacement after filtering. By using the equilibrium system space transformation and the equilibrium truncation method,a low-dimension controller is established with AMD at the top of the tower,which can effectively preserve the main dynamic characteristics of the structure. Taking a 700-meter-high tower under construction as an example,the active control simulation analysis under wind-induced vibration and earthquake is carried out. The results show that the control effect of the low-dimension controller with a few displacement states as feedback is basically the same as that of the full-dimension controller based on full state feedback,which can be used as an active control strategy for high tower structures.

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高塔结构振动适合也需要主动控制技术,但需要满足位移直接观测和单点控制下的低维高效控制。本文应用现代摄像观测技术,建立了一套从塔内实时连续观测振动位移的方法,为高塔结构振动主动控制提供了最直接的位移观测器,避免了以往只能建立加速度观测器,经滤波后再与假定的外荷载结合计算输出位移的冗长过程。基于均衡系统空间变换和平衡截断法,对顶端设置主动质量块的高塔结构进行降维,可以有效保留结构的主要动力特性。以某施工状态的700 m高塔为例,进行了风振和地震的主动控制仿真分析。结果表明:以少数位移状态作为反馈信号的降维控制器的控制效果和基于全状态反馈的全维控制器基本一致,可以作为高塔结构振动的主动控制策略。

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张弘毅(1991—),男,博士,副教授。E-mail:
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文茜茜(1993—),女,博士研究生。Email:

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文茜茜(1993—),女,博士研究生。Email:

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注:○表示全维控制系统;▲表示低维控制系统。

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The first nine order modal parameters of finite element model

, figureFileSmall=null, figureFileBig=null, tableContent=
模态阶数频率/Hz周期/sz向累计质量参与系数振型描述
10.06515.380.560z向1阶平动
20.08411.900.560x向1阶平动
30.2164.630.818z向2阶平动
40.2184.590.818x向2阶平动
50.5691.760.818x向3阶平动
60.5741.740.885z向3阶平动
70.8821.130.885塔体1阶扭转
81.1130.900.885x向4阶平动
91.1230.890.921z向4阶平动
), ArticleFig(id=1228634288682365514, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228634261973037619, language=CN, label=表1, caption=

有限元模型前9阶模态参数

, figureFileSmall=null, figureFileBig=null, tableContent=
模态阶数频率/Hz周期/sz向累计质量参与系数振型描述
10.06515.380.560z向1阶平动
20.08411.900.560x向1阶平动
30.2164.630.818z向2阶平动
40.2184.590.818x向2阶平动
50.5691.760.818x向3阶平动
60.5741.740.885z向3阶平动
70.8821.130.885塔体1阶扭转
81.1130.900.885x向4阶平动
91.1230.890.921z向4阶平动
), ArticleFig(id=1228634288783028816, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228634261973037619, language=EN, label=Tab.2, caption=

Basic information of seismic waves

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序号年份地震事件观测站震级机制
11979Imperial Valley-06Calipatria Fire Station6.53strike slip
21987Superstition Hills-02El Centro Imp. Co. Cent6.54strike slip
31992LandersDesert Hot Springs7.28strike slip
41966ParkfieldCholame-Shandon Array 12#6.19strike slip
51968Borrego MtnEl Centro Array 9#6.63strike slip
61971San FernandoWhittier Narrows Dam6.61Reverse
71980Victoria, MexicoChihuahua6.33strike slip
81980Irpinia, Italy-01Bagnoli Irpinio6.90Normal
91978TabasTabas7.35Reverse
101935Helena, Montana-01Carroll College6.00strike slip
111992Cape MendocinoPetrolia7.01Reverse
121999Kocaeli, TurkeyIzmit7.51strike slip
131940Imperial Valley-02El Centro Array 9#6.95strike slip
141974Hollister-03Hollister City Hall5.14strike slip
151983Coalinga-01Parkfield-Vineyard Cany 6W6.36Reverse
161986Chalfant Valley-02Mammoth Lakes Sheriff Subst.6.19strike slip
171986Kalamata, Greece-02Kalamata (bsmt) (2nd trigger)5.40Normal
181987Whittier Narrows-01Newhall-W Pico Canyon Rd.5.99Reverse Oblique
191989Loma PrietaGilroy Array 2#6.93Reverse Oblique
201994Northridge-01LA-Pico & Sentous6.69Reverse
), ArticleFig(id=1228634288875303512, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228634261973037619, language=CN, label=表2, caption=

地震波基本信息

, figureFileSmall=null, figureFileBig=null, tableContent=
序号年份地震事件观测站震级机制
11979Imperial Valley-06Calipatria Fire Station6.53strike slip
21987Superstition Hills-02El Centro Imp. Co. Cent6.54strike slip
31992LandersDesert Hot Springs7.28strike slip
41966ParkfieldCholame-Shandon Array 12#6.19strike slip
51968Borrego MtnEl Centro Array 9#6.63strike slip
61971San FernandoWhittier Narrows Dam6.61Reverse
71980Victoria, MexicoChihuahua6.33strike slip
81980Irpinia, Italy-01Bagnoli Irpinio6.90Normal
91978TabasTabas7.35Reverse
101935Helena, Montana-01Carroll College6.00strike slip
111992Cape MendocinoPetrolia7.01Reverse
121999Kocaeli, TurkeyIzmit7.51strike slip
131940Imperial Valley-02El Centro Array 9#6.95strike slip
141974Hollister-03Hollister City Hall5.14strike slip
151983Coalinga-01Parkfield-Vineyard Cany 6W6.36Reverse
161986Chalfant Valley-02Mammoth Lakes Sheriff Subst.6.19strike slip
171986Kalamata, Greece-02Kalamata (bsmt) (2nd trigger)5.40Normal
181987Whittier Narrows-01Newhall-W Pico Canyon Rd.5.99Reverse Oblique
191989Loma PrietaGilroy Array 2#6.93Reverse Oblique
201994Northridge-01LA-Pico & Sentous6.69Reverse
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基于塔内位移观测器和均衡空间低维控制器的高塔结构振动主动控制系统
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文茜茜 1 , 张弘毅 1 , 陈政清 1 , 华旭刚 1 , 牛华伟 1 , 徐赵东 2
振动工程学报 | 2024,37(7): 1126-1138
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振动工程学报 | 2024, 37(7): 1126-1138
基于塔内位移观测器和均衡空间低维控制器的高塔结构振动主动控制系统
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文茜茜1 , 张弘毅1 , 陈政清1, 华旭刚1, 牛华伟1, 徐赵东2
作者信息
  • 1湖南大学桥梁工程安全与韧性全国重点实验室, 湖南 长沙 410082
  • 2东南大学土木工程学院, 江苏 南京 211189
  • 文茜茜(1993—),女,博士研究生。Email:

通讯作者:

张弘毅(1991—),男,博士,副教授。E-mail:
Active control system of high tower based on displacement observer inside tower and low-dimension controller in equilibrium space
Xi-xi WEN1 , Hong-yi ZHANG1 , Zheng-qing CHEN1, Xu-gang HUA1, Hua-wei NIU1, Zhao-dong XU2
Affiliations
  • 1National Key Laboratory of Bridge Safety and Resilience, Hunan University, Changsha 410082, China
  • 2School of Civil Engineering, Southeast University, Nanjing 211189, China
出版时间: 2024-07-28 doi: 10.16385/j.cnki.issn.1004-4523.2024.07.005
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高塔结构振动适合也需要主动控制技术,但需要满足位移直接观测和单点控制下的低维高效控制。本文应用现代摄像观测技术,建立了一套从塔内实时连续观测振动位移的方法,为高塔结构振动主动控制提供了最直接的位移观测器,避免了以往只能建立加速度观测器,经滤波后再与假定的外荷载结合计算输出位移的冗长过程。基于均衡系统空间变换和平衡截断法,对顶端设置主动质量块的高塔结构进行降维,可以有效保留结构的主要动力特性。以某施工状态的700 m高塔为例,进行了风振和地震的主动控制仿真分析。结果表明:以少数位移状态作为反馈信号的降维控制器的控制效果和基于全状态反馈的全维控制器基本一致,可以作为高塔结构振动的主动控制策略。

主动控制  /  位移观测器  /  均衡空间  /  摄像测量  /  低维控制器

The vibration of high-rise tower structure requires active control. However,the problem of direct displacement observation and high-efficient low-dimension control strategy under single point needs to be solved. In this paper,a set of real-time continuous observation method of displacement from the inside of the tower is established by using modern video metrics technology,which provides the most direct displacement observer for the active control of the high tower. It avoids the tedious calculation process of building an acceleration observer,and then combining with the assumed external load to calculate the displacement after filtering. By using the equilibrium system space transformation and the equilibrium truncation method,a low-dimension controller is established with AMD at the top of the tower,which can effectively preserve the main dynamic characteristics of the structure. Taking a 700-meter-high tower under construction as an example,the active control simulation analysis under wind-induced vibration and earthquake is carried out. The results show that the control effect of the low-dimension controller with a few displacement states as feedback is basically the same as that of the full-dimension controller based on full state feedback,which can be used as an active control strategy for high tower structures.

active control  /  displacement observer  /  equilibrium space  /  videometrics  /  low-dimension controller
文茜茜, 张弘毅, 陈政清, 华旭刚, 牛华伟, 徐赵东. 基于塔内位移观测器和均衡空间低维控制器的高塔结构振动主动控制系统. 振动工程学报, 2024 , 37 (7) : 1126 -1138 . DOI: 10.16385/j.cnki.issn.1004-4523.2024.07.005
Xi-xi WEN, Hong-yi ZHANG, Zheng-qing CHEN, Xu-gang HUA, Hua-wei NIU, Zhao-dong XU. Active control system of high tower based on displacement observer inside tower and low-dimension controller in equilibrium space[J]. Journal of Vibration Engineering, 2024 , 37 (7) : 1126 -1138 . DOI: 10.16385/j.cnki.issn.1004-4523.2024.07.005
结构振动控制技术可分为被动控制、半主动控制和主动控制。主动控制技术具有严密的科学理论、更宽的抑制频率范围、较好的振动控制效果和多学科交叉融合的特征,一直受到学术界的高度关注。高塔结构比较适合也迫切需要主动控制技术15。如某国计划建造的观光塔,高度超过千米,预计施工期五年,施工状态的结构周期变化为5倍,且内部空间小,主动控制是保障其施工安全的理想方式。近年发展迅速的大型风力机也很需要主动控制技术,风力机塔筒长细比可达15,长期处于强烈的振动状态。传统的TMD被动控制至少需要两套设备来分别控制1,2阶模态,并且因塔内行程受限,TMD减振效果不理想。高塔结构具备主动控制需要的供电条件和设备安装条件,但也有两个特别的难题:一是振动位移测量难,二是一个作动器需要控制多阶模态振动。
主动控制需要实时识别结构振动状态,即实时准确识别振动位移和速度,为计算控制力提供依据。在信息技术不先进的时代,无法实现高塔结构位移的实时不间断测量,只能测量到加速度时程,经过滤波处理后,基于一定假设间接识别振动状态,从而大大降低了控制精度,即降低了主动控制相对被动控制的优越性。目前,在主动控制的实际应用中,可以直接采用加速度计监测结构的加速度信号作为反馈67,但都仅限于小型的模型试验;或者通过设计状态观测器来估计结构的位移和速度,进一步将估计的状态作为反馈信号计算主动控制力8。这些过程都非常繁琐,增加了控制力的计算时间,降低了控制精度。近年来,非接触式测量技术,尤其是摄像测量技术的发展为大型结构的实时状态观测提供了基础。摄像测量学是将摄影测量学原理、计算机视觉原理、光测实验力学、精密光测技术、数字图像处理与分析等学科交叉融合的一门新兴学科。它将摄像机和照相机作为一次传感器,对图像处理分析,可以测量位置、尺寸、速度和加速度等,特点是以序列图像为主要对象,实时测量运动目标的动态参数,其与接触式测量技术相比更具优势。Shang等9和Yu等10提出了一种利用串并联摄像机网络测量大型结构位移和变形的方法,通过一个相对简单的光学测量系统测量大型结构中一些感兴趣的部分的相对运动和变形。如果将摄像测量学技术应用于高塔结构的主动控制系统中,可以直接测量被控结构的位移,直接将位移状态作为反馈信号计算控制力,简化了计算步骤,并且减少了时滞,提高了计算精度。因此,现代测量与信息技术的结合为解决高塔结构位移测量难题提供了条件。
高塔结构的长细比越大,振动包含的模态数就越多,如果直接对其进行主动控制,控制器阶数会很高,导致控制计算的时间延长,控制效果降低1112,所以需要用更低维的控制器来减小计算量。模型降维这一思想虽多年前散见于一些工程界的文献中,但多出现在自动控制和电路系统领域中。土木工程领域关于模型降维的文献报道很少。Qu等13提出了一种动态凝聚法对系统进行降维,该方法的特点是在原物理空间的子空间中定义降维系统,使得状态向量具有物理意义。Cao等14在最小信息损失方法的基础上,提出了一种改进的方法,解决了原方法没有唯一的降维结果这一问题,并且使总信息损失最小。但是由上述方法得到的降维模型只保留了原始系统的高阶模态,模态质量参与比相对较小,导致低阶特性损失,而土木工程结构的动力特性一般由其低阶模态控制。平衡截断法则较好地解决了上述问题,且能够较好地保留原结构的低阶动力特性15
平衡截断法最初由Moore16提出,之后很多学者对其进行了完善和扩展1719。目前工程中常用的是经Laub等20改进的计算方法。Xie等21以柔性梁为研究对象,对平衡截断法进行了理论和实验研究,并提出了一种从物理传感器测量中提取平衡模态坐标的方法。Li等22基于改进的平衡截断法设计了一种新的低阶控制器,并用一个10层框架的数值模拟和一个单跨4层钢框架的试验验证了方法的有效性。上述数值模拟和实验表明,低阶控制模型可以有效补偿时间延迟。Chen等15和滕军等23通过比较几种常用的模型降维方法,基于平衡截断法建立了降维模型,采用区域极点配置方法设计了一种新的降维控制器。上述研究表明:平衡截断法是一种可用在土木工程领域的较为可靠的降维技术。
本文以某一高塔结构的主动控制系统为例,提出了适用于高塔结构位移监测的摄像测量法,应用平衡截断法设计了只需要前几阶位移模态的低维控制器,并进行了大量的仿真计算。
相比于传统的刚性塔,高柔塔的塔顶位移与塔高的比值要高出一倍左右,说明位移监测已经成为结构振动监测评价指标中最重要的指标之一。结构主动控制需要实时测量结构响应,采用现代控制理论计算出最优反馈控制力,在位移监测的基础上实现主动控制无疑会提高计算精度,节约计算时间。对于大型的复杂结构,采用非接触式位移测量技术是必要的。非接触式位移测量技术在工程中已经有广泛的应用,具有代表性的有GPS测量、全站仪测量、激光测量、摄像测量等24。Yu等2526提出的位移传递相机串联网络摄像测量具有多点测量、实时测量、自动化程度高以及可视化等优点,并且适合从高塔内部测量位移,在台风等灾害作用时具有很强的抗干扰能力,可用来长期实时监测高塔结构的位移和变形。
针对高塔内部相邻层之间存在遮挡的问题,将标志点和相机设置为曲线连接的形式,两个相机之间成一定角度固连,标志点和双头相机的布置如图1所示。标志点和双头相机测量编号从下往上依次为M1,S1,C1,…,Mi,Si,Ci,…,Mn,Sn,不考虑相机的旋转,双头相机所拍摄的上侧两个、下侧两个标志在水平方向位移量的计算公式为:
式中 上标“C”为双头相机编号;下标“M”,“S”为标志点编号;h为合作标志在相机图像中的位移量;为标志点的位移量;θ为双头相机的俯仰角变化量;d为双头相机与合作标志之间的距离,k为图像对标志的放大倍数,dk的数值可通过标定得到。
式(1)可列出4n个方程,存在4n+2个未知数,如果有两个及以上已知量,那么方程组可解。假设为稳定点,,方程(1)可写为如下形式:
其中:
标志点的位移可写为:
式中 
假设结构有a个自由度,需要布置b个观测点,由式(2)可知,需要b/2个双头相机,基于上述摄像测量原理测到的标志位移向量为Xsr,那么有:
式中 
式(3)即是主动控制中的输出方程,由于输出量Xsr为位移,故称为位移观测器。
位移传递摄像测量法需要将图像信号转换为数值信号,因此会在雨天、大雪天等不利环境下产生噪声影响,可根据噪声的先验信息采用自适应滤波或者卡尔曼滤波等方法进行滤波处理。
为了提高控制效率,在设计结构的主动振动控制策略时,有必要将结构模型进行一系列简化。本节首先提取原有限元模型在其中一个方向的质量和刚度矩阵,将三维有限元模型简化为平面串联质点模型,然后采用平衡截断技术由物理空间转化到均衡空间,进一步对质点模型降维建立低维控制器。三维有限元模型、平面质点模型和降维之后的质点模型的低阶动力特性基本一致。
以某施工状态的一个高塔结构(高711.36 m)为例,对多维模型进行降维分析。对图2所示的结构有限元模型进行模态分析,给出结构前9阶模态参数如表1所示,可以看出,该塔结构高,扭转响应小,以水平振动为主。为说明本文方法,在主动控制简化模型中,只考虑结构的z向来进行主动减振控制研究。
图3为平面串联质点模型,其质量矩阵为对角矩阵,取ANSYS模型对应节点的上、下节段各一半质量,采用柔度法可得到结构的柔度矩阵,对其求逆即可得到刚度矩阵。由表1可知,有限元模型z向前4阶模态累计质量参与系数已经达到了92.1%,据此图4给出了质点模型与有限元模型前4阶模态振型对比,可以看出,前4阶模态振型基本吻合,平面质点模型能够较好地反映原有限元模型z向的动力特性。
实际工程中结构简化之后自由度数仍然较多,如果直接用原结构来进行控制器设计,会导致控制器的计算时间过长,进而产生较大的时滞,导致所需要的控制力增大,控制效果降低。过大的时滞还有可能使结构响应发散,控制力单幅增大。因此需要对模型进行降维处理以方便控制器的设计,提高控制效果。
对于结构主动控制系统,需要按照状态空间的形式设计控制器,实时计算出结构所需要的控制力,从而传达给作动器对结构做出控制。对该高塔结构来说,在不考虑减振装置的情况下,结构模型在状态空间模型的阶数仍然有134个,模型的阶数比较高,因此需要进一步对简化模型进行降维。本节采用平衡截断法对简化模型进行降维。
假设线性定常控制系统的状态方程为:
式中  X为2a维状态向量;Um维控制输入向量,且U=-GXsr,其中Gm×p维矩阵;Ff维外激励荷载向量;Xp为输出状态向量,是p维向量,且Xp=[Xsr TA为2a×2a维状态矩阵;Cp×2a维输出矩阵,且p≤2aBE分别为2a×m,2a×f维作用位置矩阵。
上述系统的能控性矩阵Wr和能观性矩阵Wo分别为:
其中,表示大于0的时间常数,能控性矩阵Wr和能观性矩阵Wo分别为以下李雅普诺夫方程的解:
对上述控制系统进行坐标变换X=TZ,可以得到特征值不变的均衡系统,其中T为非奇异矩阵,代入式(4)中可以得到如下均衡系统:
其中:
根据文献[20]计算变换矩阵T的方法,有如下公式:
式中  LrLo分别为矩阵WrWo经Cholesky分解得到的下三角矩阵,即分别通过对下三角矩阵的乘积进行奇异值分解得到,即,另外, ,其中,为表征系统状态可控性与可观性的特征值,为大于0的实数;表示特征值的平方根。
均衡系统中的状态变量按照特征值由大到小排列,特征值的大小反映了系统状态的综合可控、可观性。均衡截断法即把均衡系统中较小特征值(对应结构较高模态频率)对应的状态向量舍弃掉,只保留前r阶对系统输入、输出影响较大的状态向量。现考虑所有的系统状态向量,并将均衡系统写成分块矩阵形式,原均衡系统(7)可以写为:
式中  Zr为保留的状态;Zl为舍弃的状态。
如果直接去掉式(9)第一式的第二行,降维模型可以写为:
为了尽可能多地考虑被舍弃的状态向量Zbl的影响,可以将式(9)的第一式改写为:
进而可以得到所谓考虑残差的降维模型:
式中 
分析式(8)中的非奇异转换矩阵T,可以发现矩阵第1和第2列包含了1阶模态信息,包含1阶模态振型位移和1阶模态振型速度,以此类推,矩阵Tii为奇数)和第i+1列包含了原结构第(i+1)/2阶模态。图5给出了矩阵T前4列归一化之后的结果。从图5中可以看出:前4列包含了原结构第1和第2阶模态信息。
图6给出了保留不同维数时顶层位移和加速度传递函数与原系统(OS)的接近程度。从图6中可以看出:随着r的增大,曲线重合得越多,说明保留的维数越高,传递函数越接近原系统,动力特性保留得越完整。但是维数过高则没有达到降维的目的,控制器计算过程仍然很慢,因此在满足精度要求的前提下要适当地设计保留维数。
采用平衡截断法进行降维之后,给出原模型(134维)和降维模型的极点对比如图7所示。可以看出,对模型进行非奇异变换之后形成的均衡模型极点保持不变;降维之后的低维模型可以很好地保留原模型前7阶的振型信息,而质点模型前7阶振型质量参与系数达到95.3%,因此在之后的计算中将质点模型保留14维。从图6中可以看出,在0~4 Hz内,位移传递函数和原模型基本重合,加速度传递函数偏差很小。
以施工状态的某高塔结构为背景,设计多点反馈、单点控制的AMD控制系统,在此基础上进行低维控制器的仿真,本文只验证降维控制器的有效性,为了计算方便,只选择一个方向进行控制。如图8所示,整个AMD系统位于标高为711.36 m的楼面,作用在结构的y轴方向,由质量块、作动器、导轨、弹簧、限位器和其他必要的装置组成。
由于高塔还在施工阶段,首先可以对施工阶段的结构进行过程仿真,得到结构动力参数随施工进度的变化关系;也可以在不同施工阶段对结构动力特性参数进行测试、识别,根据识别的结构参数对结构模型进行修正。上述两种方法均可得到施工阶段的模型参数,进而得到随施工阶段变化的作动器增益矩阵,用于振动控制算法。
土木工程结构的振动控制总是可以看作无限域上的行为,所以无限域时间区间[0,∞)的二次型最优控制是结构振动主动控制的理论基础。对于线性定常状态系统(4),选择半正定对称加权矩阵QR,构造二次型性能控制指标:
那么满足使J极小的最优控制向量Ut)存在且唯一,由下式决定:
其中,矩阵P为如下黎卡提方程的解:
降维过程的首要要求是精度,要求降维模型的特性与实际受控对象的特性近似度高,但仅仅满足精度要求,对于以主动控制为目的的降维来说是不够的。因为实际工程中存在这样一种情况:用精度好的降维模型设计出的控制器作用于受控对象时的控制性能,并不优于用精度稍差的降维模型设计出的控制器。因此还需考虑在降维的同时进行控制器的设计。低维模型的控制器设计有两种选择,第一种是直接对降维的低维模型进行控制器设计,第二种是对原结构进行控制器设计之后,采用平衡降维技术对整个闭环系统进行降维。
基于如式(12)所示的降维模型,根据LQR算法(14)设计的低维模型控制器为:
由于状态Zr是均衡系统下的状态,并没有实际的物理意义,必须进行如下变换与真实的状态建立联系:
式中  Xr为前r个真实状态;Xl为后l个真实状态。
将式(17)重新改写为如下形式:
式(18)中的矩阵T是在考虑实际布置的观测点位置之后由式(17)中的T重排得到的。令Zl=0,并将式(3)代入式(18),有:
将式(19)代入式(16),便可以得到低维模型控制器:
基于LQR算法(14)对原简化结构模型进行控制力设计,使用坐标变换X=TZ后,均衡系统的控制力可以写为:,然后对闭环的控制系统进行降维,控制器为:
由于在进行控制器设计之后再进行降维可能会截断AMD的信息,因此本文采用第一种控制器设计方法进行计算。
取AMD的质量为第1阶模态质量的0.8%(1000 t),为了提高AMD运行的安全性,限制AMD的行程,设置AMD系统的刚度为20 kN/m。
取高塔结构振型较大处的位移和速度向量构造降维控制器的状态向量,速度向量可由位移向量经过滤波之后微分求得,因此本文在第8,16,21,32,38,42,67个质点高度处布置标志点采集位移信号,可分别在第18,35,50个质点高度处设置双头相机。此处之所以将速度向量加入构造低维控制器的状态向量中,是因为在计算过程中发现如果只有位移向量,那么式(19)中的转换矩阵为病态矩阵,会导致控制系统极不稳定。
计算时假设结构的阻尼比为0.015,全维控制器采用的加权矩阵如下:
式中  KM分别表示结构的刚度矩阵和质量矩阵。
结构的控制力只与α/β有关,只要两者比值相同,那么控制效果就相同。取α=40000和不同的β值,对比原系统、全维控制系统和降维控制系统的复模态特征值,可以发现主动控制主要改变前几阶频率和阻尼比。图9给出了不同β值情况下前3对极点的对比,图中横坐标为系统特征值的实部,表示角频率与阻尼比的乘积,纵坐标为系统特征值的虚部,表示角频率。
图9中可以看出:(1)1个AMD控制系统至少可以实现前3阶模态的控制;(2)β值较小时,主动控制力同时改变了结构的模态频率和阻尼比,且阻尼比增加很大,意味着需要很大的能量输入;(3)随着β值的增加,模态阻尼比增加幅度减小,结构的模态频率不再发生变化,主动控制力主要以阻尼力的形式作用在结构;(4)β值越小,附加阻尼比越大,意味着需要更大的输入能量,因此要调整β值,使输入能量不过大的情况下仍然能有预期的减振效果。
脉动风实际上是空气的三维紊流,它包括顺风向、横风向和垂直向的紊流。为了方便计算,本文只考虑顺风向紊流的影响。空间上某一点的顺风向脉动风速谱采用Kaimal谱形式。
结合《建筑结构荷载规范》(GB 50009—2012)27中的体型系数μst,可得到不同高度节点上的风荷载为:
式中  μst为塔的体型系数;ρ为空气密度;Vi为各加载点的脉动风速;为各加载点的平均风速;Ai为迎风面积。
计算时,体型系数取为0.55,10 m高度处的平均风速为30.5 m/s。地面粗糙度选用A类,A类地貌的高度为300 m,当达到梯度风高度之后可认为风速不变,300 m以内按照指数率计算。
根据强风时的观察表明,一次阵风的作用在结构的迎风面上各点处的风速和风向并不是完全同步的,有的甚至是几乎无关的,因此对于建筑结构上的脉动风压必须研究其空间相关性。一般来讲,考虑到建筑结构前后风压的相关性比较复杂,从安全的角度认为结构前后的脉动风压是完全相关的,主要研究脉动风压的上下和左右相关性。
关于脉动风压频率域的相关性系数即相干函数的具体表达式形式很多,从实用、简单的角度出发,通常用到的是只与两点间距离有关的Shiotani等28在试验基础上获得的公式:
式中  分别表示垂直和水平方向脉动风压的相干系数;z-z'和y-y'分别表示垂直和水平方向两点间的距离;一般来讲,Ly取为50,Lz取为60。
对于迪拜塔,横向尺寸比竖向尺寸小很多,因此仅考虑脉动风荷载竖向的相关性。采用谐波合成法进行结构各主要点的脉动风速合成。图1011分别为仿真得到的脉动风速时程曲线和理论风速谱与模拟风速谱的对比。从图11中可以看出,模拟的风速谱与Kaimal谱吻合较好。图12为得到的风荷载时程曲线。得到每个节点上的风荷载曲线之后,分别计算结构在无控、全维控制器和低维控制器作用下的位移和加速度响应,计算过程中取α=40000,β=0.01。全维控制器和低维控制器的模拟运行时间分别为1.70和0.42 s,可见降维能够缩短控制力计算时间。被控结构顶层响应如图1314所示。
图13~17中可以看出:
(1)基于低维模型设计的控制器与全维控制器相比,计算得到的结构响应和主动控制力基本一致,在不考虑时滞的情况下,低维控制器能够较好地控制外激励荷载作用下的动态响应。
(2)低维控制器能够较好地控制顶层位移响应;无控状态下结构的顶层位移峰值和均方值分别为0.102 m,0.034 m;低维控制作用下顶层位移峰值和均方值分别为0.066 m,0.025 m;控制效果分别为35.3%和26.5%。
(3)无控状态下,顶层结构在风荷载作用下的加速度最大值为0.024 m/s2,在低维控制器作用下的加速度最大值为0.017 m/s2,控制效果为29.2%,加速度均方值由0.007降为0.004 m/s2,控制效果为42.9%。
(4)施工状态下风致振动的最大控制力不超过170 kN,AMD最大行程不超过2 m。
(5)本文设计的AMD实现了较为理想的控制效果。在实际工程中,从经济角度考虑,在满足减振需求的前提下还可进一步降低AMD的质量。
为了进一步验证降维控制器的有效性,计算了不同地震作用下的结构响应,从美国伯克利大学太平洋地震工程研究中心随机选取20条地震波进行试算,地震波的基本信息如表2所示。对地震波加速度进行比例调整,使峰值均为0.3g。取β=0.005,图18为不同地震波作用下响应的柱状图。
图18中可以看出:
(1)基于LQR算法设计的降维控制器性能稳定,与全维控制器控制效果相当,两种控制器作用下的位移响应基本一致,加速度响应略有差异。
(2)地震波作用下,控制器对位移的控制效果优于对加速度的控制效果,对均方值的控制效果优于对峰值的控制效果;在第5条地震波作用下,无控状态下的结构顶层位移最大,为1.593 m,控制器对位移峰值和均方值的控制效果分别为44.87%和61.61%;第3条地震波作用下,控制器对位移峰值的控制效果最好,为57.56%,同时控制器对位移均方值的控制效果最好,为81.23%。
(3)对加速度峰值的最佳控制效果为10.08%;在第1条地震波作用下,加速度均方值控制效果达到了47.77%,说明AMD主动控制对地震控制效果明显大于TMD被动控制,后者对抗震可能存在不利影响。
本文结合摄像测量技术,考虑残差的均衡截断法和LQR控制算法,建立了一种直接采用位移输出的高塔结构低维控制器,针对某一高耸柔性结构的主动振动控制进行了理论和模拟分析,得到的主要结论有:
(1)通过位移传递的摄像测量技术,可以实现结构状态的直接观测和实时识别,且测量精度可以达到毫米级别;在塔内观测,可提高抗环境干扰能力,并实现全天候实时连续观测,利用这一特点,建立了主动控制中的位移观测器;测量的结果可以直接作为反馈信号计算所需要的主动控制力,而不需通过加速度信号滤波、设计观测器等一系列操作对振动状态进行估计,从而提高了状态识别和控制的精度,降低了状态识别计算时滞。
(2)采用柔度法提取刚度矩阵,将有限元模型简化成串联多自由度模型,只考虑z轴方向,简化的模型周期与原结构基本一致,前4阶模态振型可以很好地吻合,有效地简化了计算过程。
(3)用考虑残差的均衡截断法降维,低维系统不仅能够保留前几阶模态的局部信息,还能尽可能还原结构的全局动力特性。对本文中的高塔结构来说,前4阶振型z向累计质量参与系数已经达到92.1%,可以基本保留原结构的动力特性;对于低维系统,保留维数越多,位移和加速度的传递函数与原结构越吻合,低维控制效果越接近全维控制器的控制效果。
(4)基于线性二次型LQR控制算法设计的低维控制器,通过选定需要控制的少数几个模态,在塔内的底层、中间层、高层布置相应数量的测量相机和标志点,只观测标志点的位移状态便可以得到主动控制力,其减振效果与全状态反馈的全维控制器减振效果基本一致,可以应用于高塔结构的主动控制系统中。
  • 国家自然科学基金联合基金资助项目(U2141242)
  • 国家自然科学基金青年基金资助项目(52208478)
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2024年第37卷第7期
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doi: 10.16385/j.cnki.issn.1004-4523.2024.07.005
  • 接收时间:2022-07-09
  • 首发时间:2026-02-12
  • 出版时间:2024-07-28
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  • 收稿日期:2022-07-09
  • 修回日期:2022-09-21
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国家自然科学基金联合基金资助项目(U2141242)
国家自然科学基金青年基金资助项目(52208478)
作者信息
    1湖南大学桥梁工程安全与韧性全国重点实验室, 湖南 长沙 410082
    2东南大学土木工程学院, 江苏 南京 211189

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张弘毅(1991—),男,博士,副教授。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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