Structural Vibration Suppression by a Neural-Network Controller with a Mass-Damper Actuator

Abstract

PID and LQR/LQG controllers have are known to be ineffective for systems suffering from parameter variations and broadband excitations. This paper presents a neural-network design for system identification and vibration suppression in a building structure with an active mass-damper. It is shown both numerically and experimentally that the neural-network controller can reliably identify system dynamics and effectively suppress vibration. For the experimental model, which has a fundamental frequency of about 0.96 Hz, the steady-state vibration amplitude under resonance and random excitation are reduced by 80% and 70%, respectively. In addition, the peak-to-peak displacement under the 7.1 Richer scale Ji-Ji earthquake, Taiwan (Sep. 21, 1999) is effectively reduced by 80%. The controller is also shown to be robust to variations in system parameters.