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System Identification of High Impact Resistant Structures

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Advances in Computational Intelligence (IWANN 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7902))

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Abstract

The main purpose of this paper is to develop numerical models for predicting and analyzing highly nonlinear behavior of integrated structure-control systems subjected to high impact loading. A time-delayed adaptive neuro-fuzzy inference system (TANFIS) is proposed for modeling complex nonlinear behavior of smart structures equipped with magnetorheological (MR) dampers under high impact forces. Experimental studies are performed to generate sets of input and output data for training and validating the TANFIS models. The high impact load and current signals are used as the input disturbance and control signals while the acceleration responses from the structure-MR damper system are used as the output signals. Comparisons of the trained TANFIS models with the experimental results demonstrate that the TANFIS modeling framework is an effective way to capture nonlinear behavior of integrated structure-MR damper systems under high impact loading.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA, 01609-2280, USA

    Yeesock Kim, K. Sarp Arsava & Tahar El-Korchi

Authors
  1. Yeesock Kim
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  2. K. Sarp Arsava
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  3. Tahar El-Korchi
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Editor information

Editors and Affiliations

  1. Department of Computer Architecture and Computer Technology, University of Granada,, Periodista Daniel Saucedo Aranda s/n,, 18071, Granada, Spain

    Ignacio Rojas

  2. Department of Electronics Technology, University of Malaga, 29071, Malaga, Spain

    Gonzalo Joya

  3. Department of Electronics Engineering, Universitat Politecnica de Catalunya, 08034, Barcelona, Spain

    Joan Gabestany

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Kim, Y., Arsava, K.S., El-Korchi, T. (2013). System Identification of High Impact Resistant Structures. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-38679-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38678-7

  • Online ISBN: 978-3-642-38679-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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