Structural vibration suppression by a neural-network controller with a mass-damper actuator

S. M. Yang, C. J. Chen, W. L. Huang

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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 languageEnglish
Pages (from-to)495-508
Number of pages14
JournalJVC/Journal of Vibration and Control
Issue number5
Publication statusPublished - 2006 May

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Materials Science(all)
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering


Dive into the research topics of 'Structural vibration suppression by a neural-network controller with a mass-damper actuator'. Together they form a unique fingerprint.

Cite this