Predicting the performance and optimizing the control parameters of an active vibration control system by pre-identifying the secondary path
JOURNAL OF SOUND AND VIBRATION(2024)
摘要
In an active vibration control (AVC) system, the secondary path can generally only be identified when the system has been built. The performance of a typical adaptive feedforward control system using the filtered-x least mean square (FxLMS) algorithm is heavily dependent upon the dynamic characteristics of the secondary path. It would, therefore, be helpful for engineers to have knowledge of this behavior early in the design process. In this paper, pre-identification of the secondary path is proposed, which means its offline identification model can be obtained without installing the AVC equipment. This can save both time and money. In the pre-identification process, the secondary path is split into three parts. The first part includes the actuator and related electrical equipment. The frequency response function of this part can be identified after the hardware has been selected. The second part involves the dynamic behavior of the structure to be controlled, which is identified by impact excitation using a specially developed pneumatic impact harmer. The third part concerns the analogue/digital and digital/analogue converters whose effect on the secondary path is offset by a phase shift corresponding to half the sampling time. In this paper, the pre-identification process of the secondary path components is described, and some experiments are conducted to verify the feasibility of the approach and to demonstrate the significance of the pre-identification results. This mainly involves predicting the control performance and optimizing the parameters of the control system. Combined with the simulation of the control system, the approach proposed can improve the work efficiency of large-scale engineering projects and reduce the cost of testing.
更多查看译文
关键词
AVC,FxLMS,Secondary path,Pre-identification,Pneumatic impact hammer,Optimization
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要