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Article
Self-organizing input space for control of structures
Smart Materials and Structures
  • Simon Laflamme, Iowa State University
  • J. J.E. Slotine, Massachusetts Institute of Technology
  • J.J. Connor, Massachusetts Institute of Technology
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
7-25-2012
DOI
10.1088/0964-1726/21/11/115015
Abstract
We propose a novel type of neural networks for structural control, which comprises an adaptive input space. This feature is purposefully designed for sequential input selection during adaptive identification and control of nonlinear systems, which allows the input space to be organized dynamically, while the excitation is occurring. The neural network has the main advantages of (1) automating the input selection process for time series that are not known a priori; (2) adapting the representation to nonstationarities; and (3) using limited observations. The algorithm designed for the adaptive input space assumes local quasi-stationarity of the time series, and embeds local maps sequentially in a delay vector using the embedding theorem. The input space of the representation, which in our case is a wavelet neural network, is subsequently updated. We demonstrate that the neural net has the potential to significantly improve convergence of a black-box model in adaptive tracking of a nonlinear system. Its performance is further assessed in a full-scale simulation of an existing civil structure subjected to nonstationary excitations (wind and earthquakes), and shows the superiority of the proposed method.
Comments

This is a manuscript from an article from Smart Materials and Structures,21(115015)2012; 1-16. Doi: 10.1088/0964-1726/21/11/115015. Posted with permission.

Copyright Owner
IOP Publishing Ltd
Language
en
Date Available
2015-06-05
File Format
application/pdf
Citation Information
Simon Laflamme, J. J.E. Slotine and J.J. Connor. "Self-organizing input space for control of structures" Smart Materials and Structures Vol. 21 Iss. 11 (2012) p. 1 - 32
Available at: http://works.bepress.com/simon_laflamme/30/