共查询到18条相似文献，搜索用时 103 毫秒
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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
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高架桥梁地震响应模糊半主动控制 总被引：3，自引：0，他引：3
陈水生 《地震工程与工程振动》2005,25(6):165-171
提出了使用MR阻尼器（Magnetorheological Damper）作为控制设备，以模糊集为基础的半主动控制算法，研究了8种模糊控制规则在高架桥梁地震响应中的控制效果。本文提出的模糊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，模糊算法的输出直接控制MR阻尼器的输入电压，与LQR-clipped算法不同，MR阻尼器的输入电压可以是零与最大值之间的任意值。根据高架桥梁的结构特点，将典型的墩-支座-桥面结构简化为一个两自由度的线性系统，计算了El Centro地震激励下，MR模糊半主动控制的地震响应，并分别与没有控制及其他控制时的地震响应进行了对比，分析了各种控制算法的控制效果。研究结果表明，MR模糊半主动控制算法可以达到LQR-clipped半主动的控制效果，且模糊控制所需要的控制力较小，为有效地发挥MR阻尼器的功能提供了一种简单的半主动算法。 相似文献
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模糊神经网络控制系统优化的实整数混合编码遗传算法 总被引：2，自引：1，他引：1
本文提出了采用实数整数混合编码的遗传算法来优化模糊神经网络控制系统的方法,内容包括：增益、输入和输出变量、控制规则的编码、解码、交叉算子和变异算子以及系统优化的实施步骤。用此方法,可以优化确定系统输入和输出变量的隶属函数以及模糊控制规则,此外,针对框架结构地震反应的主动控制,采用本文方法优化建设了模糊神经网络控制系统,并进行了仿真试验和分析。结果表明,本文方法优化的控制系统具有很好的控制效果,而且优于LQ控制算法。 相似文献
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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
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介绍了第3代结构风振控制基准问题的定义。通过观测部分楼层加速度和控制力输出,建立了模糊神经网络控制器,解决了传统控制中有限的传感器数目对系统振动状态估计的困难;利用模糊神经网络预测结构的控制行为,消除了闭环控制系统中存在的时滞;通过模糊神经网络控制器的学习功能,解决了土木工程复杂结构模糊控制中难以依据专家的主观经验来确定模糊控制规则和语言变量隶属函数等困难。以风振控制的基准问题为研究对象,编制了程序对受控系统进行数值仿真分析。分析表明,模糊神经网络控制策略能有效地抑制高层建筑的风振反应。 相似文献
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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
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本文首先分析了基于刚性地基结构体系设计的AMD控制器控制SSI体系地震反应的基本原理和方法。然后,采用LQR方法通过仿真分析对基于刚性地基结构体系设计的AMD控制器控制SSI体系的适用性进行了研究,结果表明当SSI体系的基频与刚性地基结构体系的基频比ωs/ωr大于0.9时控制效率基本和考虑SSI效应控制器的控制效果接近,当ωs/ωr值小于0.4的时候,控制效率降低比较严重,对SSI体系不能起到控制作用。接着,对4种控制器及三种土体条件SSI体系进行了AMD主动控制的振动台模型试验,试验结果表明当土体相对较硬时基于刚性地基结构体系设计的AMD控制器可以控制SSI体系的反应,但土体相对较软时这种AMD控制器不能控制SSI体系的反应,甚至放大结构的反应。最后,设计和完成了上部两层框架SSI体系地震反应AMD控制的小型振动台试验,试验结果进一步验证了仿真分析的结论。 相似文献
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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
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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
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A fuzzy logic based centralized control algorithm for irrigation canals is presented. Purpose of the algorithm is to control downstream discharge and water level of pools in the canal, by adjusting discharge release from the upstream end and gates settings. The algorithm is based on the dynamic wave model (Saint‐Venant equations) inversion in space, wherein the momentum equation is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. The fuzzy rule based model is developed on fuzzification of a new mathematical model for wave velocity, the derivational details of which are given. The advantages of the fuzzy control algorithm, over other conventional control algorithms, are described. It is transparent and intuitive, and no linearizations of the governing equations are involved. Tuning of the algorithm and method of computation are explained. It is shown that the tuning is easy and the computations are straightforward. The algorithm provides stable, realistic and robust outputs. The disadvantage of the algorithm is reduced precision in its outputs due to the approximation inherent in the fuzzy logic. Feed back control logic is adopted to eliminate error caused by the system disturbances as well as error caused by the reduced precision in the outputs. The algorithm is tested by applying it to water level control problem in a fictitious canal with a single pool and also in a real canal with a series of pools. It is found that results obtained from the algorithm are comparable to those obtained from conventional control algorithms. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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C. Sivapragasam G. Vasudevan P. Vincent P. Sugendran M. Marimuthu S. Seenivasakan 《水文研究》2007,21(21):2835-2844
Decision‐making in reservoir operation has become easy and understandable with the use of fuzzy logic models, which represent the knowledge in terms of interpretable linguistic rules. However, the improvement in interpretability with increase in number of fuzzy sets (‘low’, ‘high’, etc) comes with the disadvantage of increase in number of rules that are difficult to comprehend by decision makers. In this study, a clustering‐based novel approach is suggested to provide the operators with a limited number of most meaningful operating rules. A single triangular fuzzy set is adopted for different variables in each cluster, which are fine‐tuned with genetic algorithm (GA) to meet the desired objective. The results are compared with the multi fuzzy set fuzzy logic model through a case study in the Pilavakkal reservoir system in Tamilnadu State, India. The results obtained are highly encouraging with a smaller set of rules representing the actual fuzzy logic system. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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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
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Porosity, the void portion of reservoir rocks, determines the volume of hydrocarbon accumulation and has a great control on assessment and development of hydrocarbon reservoirs. Accurate determination of porosity from core analysis is highly cost, time, and labor intensive. Therefore, the mission of finding an accurate, fast and cheap way of determining porosity is unavoidable. On the other hand, conventional well log data, available in almost all wells contain invaluable implicit information about the porosity. Therefore, an intelligent system can explicate this information. Fuzzy logic is a powerful tool for handling geosciences problem which is associated with uncertainty. However, determination of the best fuzzy formulation is still an issue. This study purposes an improved strategy, called hybrid genetic algorithm–pattern search (GA–PS) technique, against the widely held subtractive clustering (SC) method for setting up fuzzy rules between core porosity and petrophysical logs. Hybrid GA–PS technique is capable of extracting optimal parameters for fuzzy clusters (membership functions) which consequently results in the best fuzzy formulation. Results indicate that GA–PS technique manipulates both mean and variance of Gaussian membership functions contrary to SC that only has a control on mean of Gaussian membership functions. A comparison between hybrid GA–PS technique and SC method confirmed the superiority of GA–PS technique in setting up fuzzy rules. The proposed strategy was successfully applied to one of the Iranian carbonate reservoir rocks. 相似文献
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Utilizing the rainfall intensity, and slope data, a fuzzy logic algorithm was developed to estimate sediment loads from bare soil surfaces. Considering slope and rainfall as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relations among rainfall intensity, slope, and sediment transport were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF-THEN format. The commonly used weighted average method was employed for the defuzzification procedure.The sediment load predicted by the fuzzy model was in satisfactory agreement with the measured sediment load data. Predicting the mean sediment loads from experimental runs, the performance of the fuzzy model was compared with that of the artificial neural networks (ANNs) and the physics-based models. The results of showed revealed that the fuzzy model performed better under very high rainfall intensities over different slopes and over very steep slopes under different rainfall intensities. This is closely related to the selection of the shape and frequency of the fuzzy membership functions in the fuzzy model. 相似文献
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A fuzzy‐logic control algorithm, based on the fuzzification of the MR damper characteristics, is presented for the semiactive control of building frames under seismic excitation. The MR damper characteristics are represented by force–velocity and force–displacement curves obtained from the sinusoidal actuation test. The method does not require any analytical model of MR damper characteristics, such as the Bouc‐Wen model, to be incorporated into the control algorithm. The control algorithm has a feedback structure and is implemented by using the fuzzy‐logic and Simulink toolboxes of MATLAB. The performance of the algorithm is studied by using it to control the responses of two example buildings taken from the literature—a three‐storey building frame, in which controlled responses are obtained by clipped‐optimal control and a ten‐storey building frame. The results indicate that the proposed scheme provides nearly the same percentage reduction of responses as that obtained by the clipped‐optimal control with much less control force and much less command voltage. Position of the damper is found to significantly affect the controlled responses of the structure. It is observed that any increase in the damper capacity beyond a saturation level does not improve the performance of the controller. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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We present a novel approach for optimizing reservoir operation through fuzzy programming and a hybrid evolution algorithm, i.e. genetic algorithm (GA) with simulated annealing (SA). In the analysis, objectives and constraints of reservoir operation are transformed by fuzzy programming for searching the optimal degree of satisfaction. In the hybrid search procedure, the GA provides a global search and the SA algorithm provides local search. This approach was investigated to search the optimizing operation scheme of Shihmen Reservoir in Taiwan. Monthly inflow data for three years reflecting different hydrological conditions and a consecutive 10‐year period were used. Comparisons were made with the existing M‐5 reservoir operation rules. The results demonstrate that: (1) fuzzy programming could effectively formulate the reservoir operation scheme into degree of satisfaction α among the users and constraints; (2) the hybrid GA‐SA performed much better than the current M‐5 operating rules. Analysis also found the hybrid GA‐SA conducts parallel analyses that increase the probability of finding an optimal solution while reducing computation time for reservoir operation. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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Abstract This paper describes a fuzzy rule-based approach applied for reconstruction of missing precipitation events. The working rules are formulated from a set of past observations using an adaptive algorithm. A case study is carried out using the data from three precipitation stations in northern Italy. The study evaluates the performance of this approach compared with an artificial neural network and a traditional statistical approach. The results indicate that, within the parameter sub-space where its rules are trained, the fuzzy rule-based model provided solutions with low mean square error between observations and predictions. The problems that have yet to be addressed are overfitting and applicability outside the range of training data. 相似文献