基于迭代学习控制算法的弹性板主动控制及实验研究  

Active Control of Elastic Plates Based on Iterative Learning Control Algorithms and Experimental Research

作　　者：叶可 姜金辉[1] Ye Ke;Jiang Jin-hui(State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronautics and Astronautics,Jiangsu Nanjing 210016)

机构地区：[1]南京航空航天大学机械结构力学与控制国家重点实验室,江苏南京210016

出　　处：《电子质量》2022年第1期27-33,共7页Electronics Quality

摘　　要：如何更高效地实现结构的实时振动控制一直是振动工程领域不断探索的问题,而作动器则是振动主动控制研究中极为重要的一环。该文从Hamilton原理及压电材料的力-电耦合本构方程出发,建立了线弹性压电体的有限元状态空间方程,在此基础上提出一种利用被控系统实时响应作为控制输入的迭代学习控制算法,通过MATLAB仿真验证了迭代控制对弹性板模型振动控制的有效性,此后基于NI Compact RIO及LabVIEW平台搭建了振动主动控制实验系统。在实验中,以弹性翼型板为对象,对比了速度负反馈方法与迭代学习控制方法的控制效率。验证结果表明,后者的抑振效率之于前者可提高超过62%。How to achieve real-time vibration control of structures more efficiently has been a problem constantly explored in the field of vibration engineering, and actuators are an extremely important part of the research on active vibration control. In this paper, the finite element state-space equations of a linearly elastic piezoelectric body are established from Hamilton’s principle and the force-electric coupling instanton equations of piezoelectric materials, based on which an iterative learning control algorithm using the real-time response of the controlled system as the control input is proposed. CompactRIO and LabVIEW platforms were used to build an experimental system for active vibration control. In the experiments, the control efficiency of the negative velocity feedback method and the iterative learning control method were compared with the elastic wingshaped plate as the object. The validation results show that the vibration suppression efficiency of the latter can be improved by more than 62% compared to the former.

关 键 词：弹性翼形板 振动主动控制 迭代学习控制 LABVIEW 

分 类 号：TB535[理学—物理]