共查询到19条相似文献，搜索用时 173 毫秒
1.
在模糊控制中,如何更加合理地生成控制规则,是其应用的一个重要问题。本文采用动态模糊神经网络(DFNN)算法,并借助于最优控制算法的样本数据,实现建筑结构振动控制中的模糊规则自动提取。首先,介绍了DFNN的结构和算法;其次,采用DFNN算法设计了二输入单输出及四输入单输出两种模糊控制器,对顶层设置AMD控制装置的五层钢框架模型结构进行模糊控制仿真分析。仿真结果表明,两种模糊控制器对顶层位移和加速度反应峰值的控制效果达到50%和30%以上,对地震输入和结构参数的变化均具有较好的鲁棒性;相比二输入模糊控制器,四输入模糊控制器的控制效果更好。本文研究为地震作用下建筑结构AMD模糊控制提供了新的思路和方法。 相似文献
2.
将TS模糊控制模型用于结构振动控制中,提出了一种新型的模糊控制器。利用传统LQR控制算法确定TS模糊控制器的参数,提出一种新的形成模糊控制规则的方法,克服了TS模糊控制器参数较多,规则难以确定的缺点;并结合一座三层钢框架模型,进行仿真分析,验证了提出的方法的有效性。 相似文献
3.
本文针对高层建筑风振控制问题,应用基于遗传算法优化模糊规则库的模糊控制方法,通过MR阻尼器实现减小高层建筑风振反应. 采用双输入、单输出的模糊控制策略, 即以风荷载和其变化率为输入量, 以MR阻尼器所提供的控制力为输出量.利用基于遗传算法的优化的模糊规则库,根据作用模糊子集的推理方法进行模糊推理运算, 并采用常用的重心法进行解模糊处理.以某12层框架结构为例, 进行数值模拟分析,并与优化前的模糊控制策略和LQR最优控制策略进行比较.数值分析结果表明,利用遗传算法使优化模糊规则库得以优化,改善了模糊控制的效果,有效地减小了结构的风振反应. 相似文献
4.
《地震工程与工程振动》2015,(6)
本文针对非线性结构振动控制问题,提出了一种将线性二次型最优(LQR)控制算法和模糊控制算法相结合的自适应减震控制方法。以原线性结构,即名义系统作为参考模型,基于参考模型设计了LQR控制器,并利用遗传算法优化LQR控制器的加权系数;将结构振动中的非线性部分作为不确定参数,以此来设计模糊控制器,弥补了结构非线性部分对振动控制的影响。最后,通过钢筋混凝土非线性结构算例验证本文所提算法的有效性。结果表明:强震作用下,结构构件会产生屈服进入非线性阶段,而基于线性参考模型设计的LQR控制器并不适用于非线性结构;模糊控制器可以补偿结构非线性产生的影响,达到自适应减震控制的目的。 相似文献
5.
结构振动的模糊建模与模糊控制规则提取 总被引：10，自引：0，他引：10
模糊振动控制中存在的模糊控制规则的建立大都依赖于主观经验的现状。对此本文提出了一种通过对结构振动模糊建模来产生控制规则的方法。首先，通过对系统运动状态变量的模糊化，建立结构振动的模糊关系模型；其次通过对结构振动的模糊关系模型的分析，提取出模糊控制规则；最后，通过一个单自由度体系的数值仿真方法进行了验证。 相似文献
6.
7.
针对当前采用PID控制器控制无人驾驶救援车伺服系统时存在的轨迹跟踪精度不高,误差控制性能较差,灵活性、平稳性和安全性能不佳等问题,提出并设计基于BP神经网络整定PID控制器的无人驾驶救援车伺服控制系统,建立突发地震灾害中无人驾驶救援车伺服控制系统驱动模型,并以此模型作为被控对象;根据系统期望输出值与实际输出值构成的控制偏差获得PID控制规律,并通过调节PID控制器控制参数实现系统控制,在此基础上,采用BP神经网络通过对无人驾驶救援车伺服控制系统性能的学习,构建基于BP神经网络整定的PID控制器,并采用梯度下降法修正控制器加权系数,通过在线调整BP神经网络加权系数即可实现控制器的自适应调整,控制突发地震灾害中无人驾驶救援车实施救援。实验结果表明,设计的基于BP神经网络整定PID控制器的无人驾驶救援车伺服系统可有效提高轨迹跟踪精度,具有较好的灵活性,且能够保证驾驶员的安全和车辆平稳行驶。 相似文献
8.
本文针对建筑结构地震响应半主动控制问题,应用基于遗传算法优化模糊规则库的遗传—模糊控制方法,通过MR阻尼器实现减小建筑结构地震响应。将结构的位移和加速度响应峰值控制双重指标作为目标函数,运用遗传算法的基本操作得到一组优化的模糊推理规则。以结构位移、加速度、地震加速度信号作为输入量,以MR阻尼器所提供的控制力为输出量,分别构造单阻尼器和多阻尼器的模糊控制策略。以某3层和6层框架结构为例,分别对两种遗传—模糊控制算法进行数值仿真分析,并与LQR最优控制结果进行比较。数值分析结果表明,采用遗传—模糊算法能够有效地减小结构的地震响应。 相似文献
9.
工程结构地震响应模糊半主动控制 总被引：3，自引：1，他引：2
提出了使用MR阻尼器（Magnetorheological Damper）作为控制设备,模糊集为基础的半主动控制算法,并运用提出的算法对土木工程结构地震响应进行了振动控制分析.本文方法的优势在于算法自身的鲁棒性、处理非线性问题的能力和不需要结构的精确数学模型,算法需要的输入变量少,可以解决实际工程中结构响应信息难以测量的困难.模糊算法的输出直接控制MR阻尼器的输入电压,控制器的计算非常简单且易于在工程中实现.本文以一个3层框架结构为算例,分析了本文算法与前人研究算法的异同.数值结果表明,本文提出的模糊半主动控制具有较高的效率,可以减小需要的控制力,充分使用了MR阻尼器的输入电压可以调节的功能,使MR阻尼器的功能得到了更好的发挥. 相似文献
10.
结构模糊控制规则优化生成的遗传算法 总被引：2，自引：0，他引：2
本文采用实数个体编码解码、两点交叉、两点变异、保留最优个体的模糊遗传算法对模糊控制规则进行优化;其次,对三层框架结构的模糊遗传算法控制进行了仿真实验,同时,与经验规则的模糊控制效果进行了比较;最后,对结构模糊遗传算法的鲁棒性进行了仿真试验,进一步验证了本文所提方法的可行性和有效性. 相似文献
11.
Applying active control systems to civil engineering structures subjected to dynamic loading has received increasing interest. This study proposes an active pulse control model, termed unsupervised fuzzy neural network structural active pulse controller (UFN‐SAP controller), for controlling civil engineering structures under dynamic loading. The proposed controller combines an unsupervised neural network classification (UNC) model, an unsupervised fuzzy neural network (UFN) reasoning model, and an active pulse control strategy. The UFN‐SAP controller minimizes structural cumulative responses during earthquakes by applying active pulse control forces determined via the UFN model based on the clusters, classified through the UNC model, with their corresponding control forces. Herein, we assume that the effect of the pulses on structure is delayed until just before the next sampling time so that the control force can be calculated in time, and applied. The UFN‐SAP controller also averts the difficulty of obtaining system parameters for a real structure for the algorithm to allow active structural control. Illustrative examples reveal significant reductions in cumulative structural responses, proving the feasibility of applying the adaptive unsupervised neural network with the fuzzy classification approach to control civil engineering structures under dynamic loading. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
12.
This paper investigates the seismic response control of a 20-story nonlinear benchmark building with a new recentering variable friction device (RVFD). The RVFD combines energy dissipation capabilities of a variable friction damper (VFD) with the recentering ability of shape memory alloy (SMA) wires. The VFD that is the first subcomponent of the hybrid device consists of a friction generation unit and piezoelectric actuators. The clamping force of the VFD can be adjusted according to the current level of ground motion by adjusting the voltage level of piezoelectric actuators. SMA wires that exhibit a unique hysteretic behavior and full shape recovery after experiencing large strains is the second subcomponent of the hybrid device. Numerical simulations of a seismically excited 20-story benchmark building are conducted to evaluate the performance of the hybrid device. A continuous hysteretic model is used to capture frictional behavior of the VFD and a neuro-fuzzy model is employed to describe highly nonlinear behavior of the SMA components of the hybrid device. A fuzzy logic controller is developed to adjust the voltage level of VFDs for favorable performance in an RVFD hybrid application. Results show that the RVFD modulated with a fuzzy logic control strategy can effectively reduce interstory drifts and permanent deformations without increasing acceleration response of the benchmark building for most cases. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
13.
High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper (TMD) can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom (MDF) systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building. 相似文献
14.
Traditional control strategies have difficulty handling nonlinear behavior of structures, time variable features and parameter uncertainties of structural control systems under seismic excitation. An off-and-towardsequilibrium （OTE） strategy combined with fuzzy control is presented in this paper to overcome these difficulties. According to the OTE strategy, the control force is designed from the viewpoint of a mechanical relationship between the motions of the structure, the exciting force and the control force. The advantage of the OTE strategy is that it can be used for a variety of control systems. In order to evaluate the performance of the proposed strategy, the seismic performance of a three-story shear building with an Active Tendon System （ATS） using a Fuzzy Logic Controller （FLC） is studied. The main advantage of the fuzzy controller is its inherent robustness and ability to handle any nonlinear behavior of structures. However, there are no design guidelines to set up the corresponding control rule table for a FLC. Based on the proposed strategy for the FLC, a control rule table associated with the building under study is developed, which then allows formation of a detailed algorithm. The results obtained in this study show that the proposed strategy performs slightly better than the linear quadratic regulator （LQR） strategy, while possessing several advantages over the LQR controller. Consequently, the feasibility and validity of the proposed strategy are verified. 相似文献
15.
屋盖MR智能隔震系统对升船结构顶部厂房地震鞭梢效应的模糊控制 总被引：8，自引：0，他引：8
基于抑制升船结构顶部厂房地震鞭梢效应的目的，本文提出了升船结构顶部厂房屋盖MR智能隔震模糊控制的思想。文中，在建立屋盖智能隔震升船结构计算力学模型的基础上，建立了屋盖MR智能隔震系统对升船结构顶部厂房地震反应模糊控制的设计计算方法。文中并以中国某大坝巨型升船结构为背景，设计了屋盖MR智能隔震系统对升船结构顶部厂房地震反应模糊控制的控制系统。仿真分析和对MR阻尼器的参数研究表明，安装合适的屋盖MR智能隔震系统并采用模糊控制策略能有效地抑制具有不确定参数升船结构顶部厂房地震反应的鞭梢效应，且模糊控制器能保持较好的稳定性能。 相似文献
16.
Fuzzy control of piezoelectric friction dampers for seismic protection of smart base isolated buildings 总被引：1，自引：0，他引：1
This study proposes two fuzzy logic controllers (FLCs) for operating control force of piezoelectric friction dampers used
for seismic protection of base-isolated buildings against various types of earthquake excitations. The first controller employs
a hierarchic control strategy in which a higher-level supervisory controller operates a single sub-level FLC by modifying
its input normalization factors in order to determine command voltage of the damper according to current level of ground motion.
The second controller is a self organizing FLC that employs genetic algorithms in order to build a knowledge base for the
fuzzy controller. Numerical simulations of a base-isolated building are conducted to evaluate the performance of the controllers.
For comparison purposes, an optimal controller is also developed and considered in the simulations together with maximum passive
operation of the friction damper. Results for several historical ground motions show that developed fuzzy logic controllers
can effectively reduce isolation system deformations without the loss of potential advantages of seismic base isolation. 相似文献
17.
Due to their intrinsically nonlinear characteristics, development of control strategies that are implementable and can fully
utilize the capabilities of semiactive control devices is an important and challenging task. In this study, two control strategies
are proposed for protecting buildings against dynamic hazards, such as severe earthquakes and strong winds, using one of the
most promising semiactive control devices, the magnetorheological (MR) damper. The first control strategy is implemented by
introducing an inverse neural network (NN) model of the MR damper. These NN models provide direct estimation of the voltage
that is required to produce a target control force calculated from some optimal control algorithms. The major objective of
this research is to provide an effective means for implementation of the MR damper with existing control algorithms. The second
control strategy involves the design of a fuzzy controller and an adaptation law. The control objective is to minimize the
difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper.
The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the
control strategy based on the combination of the fuzzy controller and the adaptation law doesn’t require a prior knowledge
of the combined building-damper system, this approach provides a robust control strategy that can be used to protect nonlinear
or uncertain structures subjected to random loads.
Supported by: Hong Kong Research Grant Council Competitive Earmarked Research Grant HKUST 6218 / 99E and by the National Science Foundation
under grant CMS 99-00234. 相似文献
18.
Based on the genetic algorithms (GAs), a fuzzy sliding mode control (FSMC) method for the building structure is designed in this research. When a fuzzy logic control method is used for a structural system, it is hard to get proper control rules directly, and to guarantee the stability and robustness of the fuzzy control system. Generally, the fuzzy controller combined with sliding mode control is applied, but there is still no criterion to reach an optimal design of the FSMC. In this paper, therefore, we design a fuzzy sliding mode controller for the building structure control system as an optimization problem and apply the optimal searching algorithms and GAs to find the optimal rules and membership functions of the FSMC. The proposed approach has the merit to determine the optimal structure and the inference rules of fuzzy sliding mode controller simultaneously. It is found that the building structure under the proposed control method could sustain in safety and stability when the system is subjected to external disturbances. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
19.
The neuro‐controller training algorithm based on cost function is applied to a multi‐degree‐of‐freedom system; and a sensitivity evaluation algorithm replacing the emulator neural network is proposed. In conventional methods, the emulator neural network is used to evaluate the sensitivity of structural response to the control signal. To use the emulator, it should be trained to predict the dynamic response of the structure. Much of the time is usually spent on training of the emulator. In the proposed algorithm, however, it takes only one sampling time to obtain the sensitivity. Therefore, training time for the emulator is eliminated. As a result, only one neural network is used for the neuro‐control system. In the numerical example, the three‐storey building structure with linear and non‐linear stiffness is controlled by the trained neural network. The actuator dynamics and control time delay are considered in the simulation. Numerical examples show that the proposed control algorithm is valid in structural control. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献