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Adaptive Neural Network Controller For Nonlinear Highway Bridge Benchmark

Submitted2019-03-14
Last Update2019-03-14
TitleAdaptive Neural Network Controller For Nonlinear Highway Bridge Benchmark
Author(s)Author #1
Author title:
Name: Ahmad Y. Rababah
Org: Civil Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
Country:
Email: test@test.com

Author #2
Author title:
Name: Khaldoon A. Bani-Hani
Org: Civil Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
Country:
Email:

Author #3
Author title:
Name: Wasim S. Baraham
Org: Civil Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
Country:
Email:

Other Author(s)
Contact AuthorAuthor #1
Alt Email: test@test.com
Telephone:
KeywordsNeural networks, Active control, Highway bridge benchmark, LQG, Bridge, Vibration control, Dynamic system modeling, Acceleration sensor, Smart structures
AbstractIn this paper, a neural network-based active control algorithm is proposed and evaluated for a seismically excited highway bridge. A nonlinear three-dimensional highway bridge model equipped with 16 active hydraulic actuators placed orthogonally between the deck-ends and the abutments is employed to demonstrate and evaluate the developed method. The control strategy proposes a training emulator neural network model that operates online to generate the training data for the controller. The neural network controller is trained by the aid of the emulator neural network and by back propagating the control signal error through the emulator neural network. An H2/LQG control algorithm is designed for the bridge and results are compared to those of the proposed method. Performance indices for the benchmark bridge response are defined, computed and compared. The results revealed that the controller was quite effective in seismic response reduction for a wide range of ground motions. Also, it was robust and stable enough so that it was not sensitive to either sensor noise or sensor failure.
Paperview paper 4651.pdf (2144KB)

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