Share Email Print

Proceedings Paper

Application of self-tuning Gaussian networks for control of civil structures equipped with magnetorheological dampers
Author(s): Simon Laflamme; Jerome J. Connor
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 6 April 2009
PDF: 12 pages
Proc. SPIE 7288, Active and Passive Smart Structures and Integrated Systems 2009, 72880M (6 April 2009); doi: 10.1117/12.815540
Show Author Affiliations
Simon Laflamme, Massachusetts Institute of Technology (United States)
Jerome J. Connor, Massachusetts Institute of Technology (United States)

Published in SPIE Proceedings Vol. 7288:
Active and Passive Smart Structures and Integrated Systems 2009
Mehdi Ahmadian; Mehrdad N. Ghasemi-Nejhad, Editor(s)

© SPIE. Terms of Use
Back to Top