Vibration Suppression of a High-Rise Building With Adaptive Iterative Learning Control

Area of Science:

• Structural Engineering
• Control Systems Engineering
• Applied Mathematics

Background:

• High-rise buildings are susceptible to vibrations induced by external forces like high winds.
• Modeling buildings as distributed parameter systems is crucial for accurate dynamic analysis.
• Active Mass Dampers (AMDs) are effective devices for mitigating structural vibrations.

Purpose of the Study:

• To design an adaptive iterative learning controller (AILC) for high-rise buildings equipped with AMDs.
• To suppress vibrations in flexible building systems subjected to boundary disturbances.
• To validate the controller's performance through theoretical analysis and experimental simulations.

Main Methods:

• Modeling high-rise buildings as distributed parameter systems using partial and ordinary differential equations.
• Developing an adaptive iterative learning control algorithm for vibration suppression.
• Proving the convergence of the AILC approach through rigorous theoretical analysis.
• Conducting simulations and experiments to evaluate the controller's efficiency.

Main Results:

• The proposed AILC scheme effectively suppresses wind-induced vibrations in high-rise buildings.
• Theoretical analysis confirmed the convergence and stability of the AILC algorithm.
• Simulations and experimental results demonstrated the quantitative efficiency of the AILC scheme using root-mean-square values.

Conclusions:

• The adaptive iterative learning controller is a viable and efficient solution for mitigating vibrations in high-rise buildings with AMDs.
• The study highlights the importance of distributed parameter system modeling for accurate structural control.
• The AILC approach offers a robust method for enhancing the seismic and wind-resistant performance of tall structures.