Coarse-Fine Self-Learning Active Mass Damper for Frequency Tracking Vibration Control

Abstract

This paper presents a coarse-fine self-learning active mass damper (AMD) for frequency tracking vibration control. Even though the AMD is under unknown disturbance, it can obtain the exact exciting frequency of primary system after several self tries and achieve significant vibration suppression. The frequency tuning algorithm consists of two improved techniques, namely, the variable-gain frequency estimator and variable learning rate [Formula: see text]-learning algorithm, both aim to accelerate the convergence speed of frequency estimation. The coarse tuning method helps to tune the AMD’s natural frequency in a relatively wide effective bandwidth using frequency estimator and the fine tuning method can achieve precise tune in a tiny-scale bandwidth. The simulation results demonstrate that this coarse-fine learning AMD can help obtain the exact exciting frequency after several times of self-learning, and the vibration of primary system is dramatically attenuated by tuning the AMD’s natural frequency to match with the exciting frequency. This is the first time for the tuning method of mass damper proposed using machine learning, which can help obtain the exact exciting frequency after several self-tries.