基于神经网络的压电自感知主动控制仿真试验

Active Vibration Control Using Piezoelectric Self-Sensing Actuator Based on Neural Network

由于传统的自感知电桥电路难以有效地实现自感知执行器传感与执行之间的信号分离,因此,提出了一种采用神经网络分离出压电自感知执行器传感信号的新方法,用神经网络直接估算结构的振动速度来得到振动控制时控制器的反馈信号。将嵌入在复合材料悬臂梁中的压电陶瓷片作为自感知执行器,采用基于Filter-X LMS算法的自适应滤波控制器对悬臂梁的振动进行主动控制。结果表明神经网络估算得到的振动速度和用传感器直接测得的完全吻合,将神经网络的输出作为控制器的反馈信号可以取得理想的减振效果。

A piezoelectric self-sensing actuator is realized based on a neural network for separating the strain rate signal from the control voltage,since the conventional strain rate bridge circuit is difficult to be adjusted to the balance.A vibration velocity estimation system using a neural network is developed.In the new system,a piezoelectric element embedded in a cantilever composite beam is used as SSA,an adaptive feedback controller is constructed by using the Filtered-X LMS algorithm for the active vibration control.Experimental results show that the estimated vibration velocity of cantilever beam agrees well with the vibration velocity directly measured by a laser displacement sensor,and the new system exhibits good damping performance in the vibration control of the cantilever composite beam.