Development of a neural classifier with genetic algorithm for structural vibration suppression

On September 21, 1999, Taiwan was slammed by Taiwan´s biggest quake since 1935. The magnitude 7.6 tremor with its epicenter in central Nantou County killed more than 2,300 persons and damaged 82,000 housing units. With the trend toward taller and more flexible building structures, the use of vibration control devices, passive as well as active, as means of structural protection against strong wind and earthquakes have received significant attention in recent years. A mass-damper shaking table system has been considered as means for vibration suppression to external excitation and disturbances. In this paper, the direct experiment method is adopted to determine the control gains for better performance index. No explicitly system identification of the plant dynamics, no membership function and thus no fuzzification-defuzzification operation are required. For effective control performance, a neural classifier controller with genetic algorithm is developed. Compared with the conventional PI controller, neural network and fuzzy controller, the neural classifier controller using genetic algorithm has been presented with the effectiveness of the vibration suppression control. Experimental results show that the neural classifier controller remains effective for building structure vibration suppression under free vibration and forced vibration excitation.