System Identification Of Smart Structures
Author(s)
Y. Kim, T. El-Korchi & K. S. Arsava
Abstract
Y. Kim, T. El-Korchi & K. S. Arsava
Department of Civil and Environmental Engineering,
Worcester Polytechnic Institute (WPI), Worcester, MA, USA
Abstract
This paper proposes a fuzzy model for predicting nonlinear behaviour of smart
structures. The parameters of the fuzzy model are trained using the
backpropagation neural network and least squares algorithms. To demonstrate
the effectiveness of the proposed Takagi-Sugeno (TS) fuzzy model, a structure
equipped with magnetorheological (MR) dampers is constructed and
investigated. Various levels of high impact loads and current signals are used as
disturbances and control signals, respectively. It is demonstrated from the
experimental studies that the proposed TS fuzzy model is effective in estimating
the high impact responses of the smart structural systems subjected to a variety
of high impact loads.
Keywords: Takagi-Sugeno (TS) fuzzy model, neural network, high impact loads,
magnetorheological (MR) damper, structural control.
Keywords
Takagi-Sugeno (TS) fuzzy model, neural network, high impact loads,
magnetorheological (MR) damper, structural control
Related Book
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