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Adaptive Digital Control Based on Reduced-Order Parameter Identification and Disturbance Observer for Linear Motor

[+] Author Affiliations
Chin Sheng Chen, Shih Chieh Lin, Yong Ming Hong

National Taipei University of Technology, Taipei, Taiwan

Shih Ming Wang

Chung Yuan Christian University, Chung-Li, Taiwan

Paper No. MSEC2007-31069, pp. 751-758; 8 pages
doi:10.1115/MSEC2007-31069
From:
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • Atlanta, Georgia, USA, October 15–18, 2007
  • Conference Sponsors: Manufacturing Division
  • ISBN: 0-7918-4290-8 | eISBN: 0-7918-3809-9
  • Copyright © 2007 by ASME

abstract

This paper proposes a high-efficiency digital servo driver that realizes an auto-tuning feedback and feedforward controller design using on-line parameters identification. Firstly, the variant inertia constant, damping constant and the disturbed load torque of the linear motor are estimated by a reduced-order recursive least square (RLS) estimator, which is composed of a reduced-order RLS estimator and a disturbance torque compensator (DOC). Because one plant parameter is almost constant in high sampling rate digital control for linear motors, the reduced-order RLS estimator will only calculate one plant parameter for efficiency purposes. Furthermore, the auto-tuning algorithm for the feedback and feedforward controller is realized according to the estimated parameters to match the tracking specification. The proposed auto-tuning digital servo controller is evaluated and compared experimentally with a traditional controller on a microcomputer-controlled linear motor positioning system. The experimental results show that this auto-tuning digital servo system remarkably reduces the tracking error of a controlled machine.

Copyright © 2007 by ASME
Topics: Linear motors

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