Full Content is available to subscribers

Subscribe/Learn More  >

Neural Network Predictive Control for Piezoelectric Smart Structures

[+] Author Affiliations
Jingjun Zhang, Ercheng Wang, Ruizhen Gao

Hebei University of Engineering, Hebei, China

Paper No. ESDA2008-59094, pp. 417-421; 5 pages
  • ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis
  • Volume 2: Automotive Systems; Bioengineering and Biomedical Technology; Computational Mechanics; Controls; Dynamical Systems
  • Haifa, Israel, July 7–9, 2008
  • Conference Sponsors: International
  • ISBN: 978-0-7918-4836-4 | eISBN: 0-7918-3827-7
  • Copyright © 2008 by ASME


The piezoelectric smart structure is a force-electric coupling structure, and piezoelectric patches can not be patched ideally, so it is difficult to build the accurate mathematical model of piezoelectric smart structure. The traditional vibration control methods depend on the structural mathematical model, and the control result is unsatisfactory. Considering this problem, this paper introduces the nonlinear generalized predictive control algorithm based on neural network predictive model into piezoelectric smart structure. Because of the difficulties of building the mathematical model and extracting dynamic data from experiment, the finite element software (ANSYS) is employed to analyze and obtain the dynamic response data of piezoelectric smart structure through modal analysis and transient analysis. Neural network predictive model of structure is built through off-line training on the basis of the data. The nonlinear generalized predictive control based on neural network has a better ability to solve complex nonlinear problem. Then the author introduces the Neural Network Based System Identification Toolbox (NNSYSID) and Neural Network Based Control System Design Toolkit (NNCTRL), which are two special toolboxes for designing neural network control system and can save lots of time for designers who can commit themselves to sixty-four-dollar question. At last, the author shows the method through a case. A cantilever beam which surface is boned piezoelectric patches used for sensor and actuator respectively is analyzed by ANSYS and controled by the neural network predictive control algorithm on the platform of NNSYSID and NNCTRL. This is a simple and effective method for designers to solve the vibration control problem of piezoelectric smart structure.

Copyright © 2008 by ASME



Interactive Graphics


Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In