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Feedforward Neural Network Compensation and State Estimation in Suppressing Mechanical Vibrations in a PMLSM

[+] Author Affiliations
M. Hirvonen, H. Yousefi, H. Handroos

Lappeenranta University of Technology

Paper No. IMECE2005-80745, pp. 1841-1848; 8 pages
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Dynamic Systems and Control, Parts A and B
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 0-7918-4216-9 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME


Neural networks have been proposed for the control and compensation of electromechanical systems during the past decade. In this study, a neural network compensator for suppressing mechanical vibrations in a Permanent Magnet Linear Synchronous Motor (PMLSM) application is postulated. The linear motor itself is controlled by a conventional PI-velocity controller, and the vibration of the flexible mechanism on the linear motor is suppressed from an outer control loop using a compensation signal from a feedforward neural network. The neural network compensation is based on feedback, which is produced from the acceleration estimation of a moving mass by using the Kalman Filter. The algorithm is first designed in the simulation program, and then implemented in the physical linear motor using a DSP application. The results of the responses are presented in this paper. The proposed method is robust in all conditions and satisfies all control evaluation criteria.

Copyright © 2005 by ASME



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