Application of self-tuning Gaussian networks for control of civil structures equipped with magnetorheological dampers

This paper proposes an adaptive neural network composed of Gaussian radial functions for mapping the behavior of civil structures controlled with magnetorheological dampers. The online adaptation takes into account the limited force output of the semi-active dampers using a sliding mode controller,...

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Bibliographic Details
Main Authors: Laflamme, Simon (Contributor), Connor, Jerome J. (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering (Contributor)
Format: Article
Language:English
Published: Society of Photo-Optical Instrumentation Engineers, 2010-03-16T20:46:32Z.
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Online Access:Get fulltext
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100 1 0 |a Laflamme, Simon  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Civil and Environmental Engineering  |e contributor 
100 1 0 |a Connor, Jerome J.  |e contributor 
100 1 0 |a Laflamme, Simon  |e contributor 
100 1 0 |a Connor, Jerome J.  |e contributor 
700 1 0 |a Connor, Jerome J.  |e author 
245 0 0 |a Application of self-tuning Gaussian networks for control of civil structures equipped with magnetorheological dampers 
260 |b Society of Photo-Optical Instrumentation Engineers,   |c 2010-03-16T20:46:32Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/52638 
520 |a This paper proposes an adaptive neural network composed of Gaussian radial functions for mapping the behavior of civil structures controlled with magnetorheological dampers. The online adaptation takes into account the limited force output of the semi-active dampers using a sliding mode controller, as their reaction forces are state dependent. The structural response and the actual forces from the dampers are used to adapt the Gaussian network by tuning the radial function widths, centers, and weights. In order to accelerate convergence of the Gaussian radial function network during extraordinary external excitations, the learning rates are also adaptive. The proposed controller is simulated using three types of earthquakes: near-field, mid-field, and far-field. Results show that the neural controller is effective for controlling a structure equipped with a magnetorheological damper, as it achieves a performance similar to the passiveon strategy while requiring as low as half the voltage input. 
546 |a en_US 
655 7 |a Article 
773 |t Proceedings of SPIE--the International Society for Optical Engineering