Neuro‐control of seismically excited steel structure through sensitivity evaluation scheme
DH Kim, IW Lee - Earthquake engineering & structural …, 2001 - Wiley Online Library
DH Kim, IW Lee
Earthquake engineering & structural dynamics, 2001•Wiley Online LibraryThe neuro‐controller training algorithm based on cost function is applied to a multi‐degree‐
of‐freedom system; and a sensitivity evaluation algorithm replacing the emulator neural
network is proposed. In conventional methods, the emulator neural network is used to
evaluate the sensitivity of structural response to the control signal. To use the emulator, it
should be trained to predict the dynamic response of the structure. Much of the time is
usually spent on training of the emulator. In the proposed algorithm, however, it takes only …
of‐freedom system; and a sensitivity evaluation algorithm replacing the emulator neural
network is proposed. In conventional methods, the emulator neural network is used to
evaluate the sensitivity of structural response to the control signal. To use the emulator, it
should be trained to predict the dynamic response of the structure. Much of the time is
usually spent on training of the emulator. In the proposed algorithm, however, it takes only …
Abstract
The neuro‐controller training algorithm based on cost function is applied to a multi‐degree‐of‐freedom system; and a sensitivity evaluation algorithm replacing the emulator neural network is proposed. In conventional methods, the emulator neural network is used to evaluate the sensitivity of structural response to the control signal. To use the emulator, it should be trained to predict the dynamic response of the structure. Much of the time is usually spent on training of the emulator. In the proposed algorithm, however, it takes only one sampling time to obtain the sensitivity. Therefore, training time for the emulator is eliminated. As a result, only one neural network is used for the neuro‐control system. In the numerical example, the three‐storey building structure with linear and non‐linear stiffness is controlled by the trained neural network. The actuator dynamics and control time delay are considered in the simulation. Numerical examples show that the proposed control algorithm is valid in structural control. Copyright © 2001 John Wiley & Sons, Ltd.
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