Optimal Neurocontroller for Nonlinear Benchmark Structure
Publication: Journal of Engineering Mechanics
Volume 130, Issue 4
Abstract
A neurocontrol method is applied to the nonlinear benchmark control problem. A neurocontroller is trained based on a reduced-order linear design model, then it is used to control a nonlinear evaluation model. In training the controller, a sensitivity evaluation scheme is used and weights are updated by minimizing a cost function. Absolute accelerations directly measured from sensors are used as the feedback signals for the controller. Not only the current step acceleration, but delay signals of sensor readings, are used to enhance the training capability. Numerical examples show that the controlled responses are considerably reduced compared with the uncontrolled case. In conclusion, the possibility of the proposed control algorithm as a candidate for the controller of nonlinear building is shown.
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References
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 130 • Issue 4 • April 2004
Pages: 424 - 429
Copyright
Copyright © 2004 American Society of Civil Engineers.
History
Received: Mar 5, 2003
Accepted: Mar 5, 2003
Published online: Mar 15, 2004
Published in print: Apr 2004
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