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Optimal Adaptive and Predictive Control System of Buildings by Neuro-Fuzzy Systems

神经模糊系统的建筑物最优自适应和预测控制系统

基本信息

批准号：
06650633
负责人：
TANI Akinori
金额：
$ 1.28万
依托单位：
Kobe University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

项目摘要

To develop the effective control system of buildings, it should be necessary to take account of special features such as complexity, uncertainty of future loading. In this research, optimal adaptive and predictive control systems of buildings against earthquake loading are developed based on fuzzy theory, neural network and genetic algorithm. These system have following special features ; (1) Target responses and control variables are described with membership function, (2) Real time prediction of earthquake input and structural identification are performed, and (3) Optimization is performed by fuzzy maximizing decision. As for the prediction of earthquake input, conditioned fuzzy set rules and neural network are employed. As for the structural identification, piece-wise linear response equations and neural network are employed. The systematic selection method of the training data of neural network for the structural identification are also developed by genetic algorithm. Objective structures are assumed 1,2 and 5-degree-of-freedom systems with active mass driver at the top of it. An equivalent variable mass system is employed as an active control method. Total active control systems for these structures are developed and digital simulations are carried out. The results show that proposed control systems can control the structural responses in accordance with the assumed membership functions. Consequently, it is proved that the proposed active control methods are effective for the control of buildings. Thorough this research, we can get al lot of basic data and systems on the fuzzy optimal control system of buildings.

为了开发建筑物的有效控制系统，有必要考虑诸如复杂性、未来荷载的不确定性等特殊特征。在这项研究中，基于模糊理论、神经网络和遗传算法，开发了建筑物抗地震荷载的最优自适应和预测控制系统。这些系统具有以下特点； (1)用隶属函数描述目标响应和控制变量，(2)进行地震输入的实时预测和结构识别，(3)通过模糊最大化决策进行优化。对于地震输入的预测，采用条件模糊集规则和神经网络。对于结构识别，采用分段线性响应方程和神经网络。遗传算法还发展了用于结构识别的神经网络训练数据的系统选择方法。假设目标结构为 1,2 和 5 自由度系统，其顶部有主动质量驱动器。采用等效变质量系统作为主动控制方法。开发了这些结构的总体主动控制系统并进行了数字模拟。结果表明，所提出的控制系统可以根据假设的隶属函数控制结构响应。结果表明，所提出的主动控制方法对于建筑物的控制是有效的。通过这项研究，我们可以获得关于建筑物模糊优化控制系统的大量基础数据和系统。