&lt;title&gt;Robust control for structural systems with uncertainties&lt;/title&gt;

Wang, Sheng-Guo; Roschke, Paul N.; Yeh, H.Y.

doi:10.1117/12.348671

Cited by 6 publications

(1 citation statement)

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“…Numerous control solutions have been proposed to control uncertain systems, including robust controllers [14,15,9,16], fuzzy logic [17][18][19], and neural networks [20][21][22]. Among these solutions, neural networks have notably gained significant popularity due to their universal approximation capability [23,24], and are promising self-contained solutions for sequential adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Self-organizing input space for control of structures

Laflamme

Slotine

Connor

2012

Smart Mater. Struct.

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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. Disciplines Civil Engineering | Construction Engineering and Management | Environmental Engineering | Structural EngineeringComments This is a manuscript from an article from Smart Materials and Structures,21 (115015) 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 identication 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 signicantly 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.

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