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.
Original language | English |
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Pages (from-to) | 495-508 |
Number of pages | 14 |
Journal | JVC/Journal of Vibration and Control |
Volume | 12 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2006 May |
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Materials Science(all)
- Aerospace Engineering
- Mechanics of Materials
- Mechanical Engineering