| Research Abstract |
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.
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