System Identification Of Smart Structures
Free (open access)
115 - 119
Y. Kim, T. El-Korchi & K. S. Arsava
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.
Takagi-Sugeno (TS) fuzzy model, neural network, high impact loads, magnetorheological (MR) damper, structural control