Optimal Adaptive and Predictive Control System of Buildings by Neural Network and Fuzzy Theory

by Akinori Tani, Kobe Univ, Kobe, Japan,
Hiroshi Kawamura, Kobe Univ, Kobe, Japan,



Document Type: Proceeding Paper

Part of: Computing in Civil and Building Engineering

Abstract: This paper describes an optimal adaptive and predictive control system and its digital simulations for a single-degree-of-freedom system subjected to earthquake loading. In this system, an active mass driver and equivalent variable mass are employed as an active control method. Prediction of earthquake input and structural identification are performed by using multi-layered neural networks based on the error back-propagation method. Off-line training is performed for these neural networks, and future earthquake inputs and structural responses of a structure are predicted. Optimization is performed by means of minimizing decision. In maximizing decision, optimal target control variables are determined by using assumed membership functions of target responses and control variables. In this system, a certain duration time is introduced as a control interval. Prediction of earthquake inputs and structural responses, and maximizing decision are performed in every control interval. Results of digital simulations-show the effectiveness of the proposed control system.

Subject Headings: Control systems | Neural networks | Earthquakes | Structural control | Building systems | Structural response | Adaptive systems | Errors (statistics)

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