噪声与振动控制

›› 2010, Vol. 30 ›› Issue (4): 144-149.DOI: 10.3969/j.issn.1006-1355.2010.04.037

• 振动噪声测试技术 • 上一篇    下一篇

《压电智能悬臂梁神经网络预测控制》

王二成，张京军，马晓雨,刘杰   

  1. （河北工程大学土木工程学院,河北邯郸056038）
  • 收稿日期:2009-12-16 修回日期:1900-01-01 出版日期:2010-08-18 发布日期:2010-08-18
  • 通讯作者: 王二成

《Neural Network Predictive Control of Cantilever Beam with Piezoelectric Sensors and Actuators》

WANG Ercheng,ZHANG Jingjun,MA Xiaoyu,LIU Jie   

  1. (College of Civil Engineering, Hebei University of Engineering, Handan Hebei 056038, China)
  • Received:2009-12-16 Revised:1900-01-01 Online:2010-08-18 Published:2010-08-18
  • Contact: WANG Ercheng

摘要： 对表面粘贴压电元件的压电智能悬臂梁进行有限元建模和分析，获取了结构的动力响应数据。根据神经网络的非线性逼近能力，用动态递归神经网络对压电振动系统进行了系统辨识，建立了系统的预测模型。以此预测模型来代替传统广义预测控制算法中的受控自回归积分滑动平均模型，对压电智能悬臂梁进行振动主动控制的研究，并优化了控制系统参数。对一单输入单输出压电智能悬臂梁系统进行了仿真分析，控制效果良好，为智能算法在智能结构中应用有一定的指导意义。

关键词: 振动与波, 智能结构, 仿真分析, 广义预测控制, 神经网络

Abstract:

Piezoelectric sensors or actuators are bonded to the surfaces of a flexible cantilever beam. The finite element method is used to build the model of the beam. The dynamic response data of the piezoelectric smart structures is obtained through the FEM analysis. According to the nonlinear approximation capability of neural networks, a dynamic recursive BP neural network is used to identify the piezoelectric vibration system, and the predictive model is built. On the basis of the neural network predictive model, the generalized predictive control algorithm is proposed to control the piezoelectric vibration system and to suppress the undesired vibration of the structures. Simulation results demonstrate the excellent performance of the developed control system. It has some significance for guiding the application of intelligent control algorithms in smart structures.

Key words: vibration and wave, smart structures, simulate analysis, the generalized predictive control algorithms, neural network

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