Novel Approach of Self-Sensing Actuation for Active Vibration Control

Abstract

In a self-sensing actuator, the same piezoelectric element functions as both a sensor and an actuator. Due to their advantages of reducing the number of needed piezoelectric elements and true collocation of sensors and actuators, self-sensing actuators have attracted the attention of many researchers and many studies have been reported. Since the actuator signal and the sensor signal are mixed together in a self-sensing actuator, usually a bridge circuit is used to separate the two signals. It has been found that the two signals cannot be separate ideally, because the bridge is difficult to balance and the actuator signal is much larger than the sensor signal. In this study, the influence of the local strain on the output signal of a piezoelectric sensor is investigated numerically and experimentally. In order to delete the influence of local strain on performance of the self-sensing actuator, a new method using neural network is proposed to identify the strain of global vibration. The identified signal is used as feedback signal for an adaptive control system and the effectiveness in suppressing the beam vibration is verified in the experiment.