LAUSR

With the trend toward taller and more flexible building structures, a mass-damper shaking table system has been considered as means for vibration suppression to external excitation and disturbances in recent years. However, there are few researches on the control of nonlinear structure using active mass damper under earthquake excitation, especially for high-rise building. This study presents a model combining the advantages of adaptive genetic algorithm and modified Newton method is developed for system identification and vibration suppression of a building structure with an active mass damper. The genetic algorithm with adaptive reproduction, crossover, and mutation operators is to search for initial weight and bias of the neural network, while the modified Newton method, similar to BFGS algorithm, is to increase network training performance. Experimental results show that the controller performance is strongly influenced by the accuracy of system identification. The controller is also shown to be robust to variations in system parameters.