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Motion Control of a Piezoelectric Microprecision Positioner Using Diagonal Recurrent Neural Networks

[+] Author Affiliations
Jua-Liang Chen

National Chung Hsing University, Taichung, Taiwan

Zong-Lin Chen

Philips & Lite-On Digital Solution Corp., Taipei, Taiwan

Paper No. DETC2010-28253, pp. 733-741; 9 pages
doi:10.1115/DETC2010-28253
From:
  • ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 4: 12th International Conference on Advanced Vehicle and Tire Technologies; 4th International Conference on Micro- and Nanosystems
  • Montreal, Quebec, Canada, August 15–18, 2010
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4412-0 | eISBN: 978-0-7918-3881-5
  • Copyright © 2010 by ASME

abstract

The objective of this paper is to design a suitable controller for a piezoelectric microprecision positioner. The dimensions of this positioner are 150 mm × 150 mm × 10 mm and with X-Y-θZ three degrees of freedoms. Piezo-electric actuators are used to drive the positioner, which is constructed by flexure structures. In order to improve the short stroke of PZT, simple-levers are added to the structures. In this research, a diagonal recurrent neural networks (DRNN) controller is added to the system with aim to reduce the effect causes by the hysteresis, inaccurate system model and phase lag, and to save time for adjusting control gains for PID control. From the experimental results, it shows that the positioning errors for the X-axis, Y-axis, and θ-axis of continuous stepping test are less than 20 nm and 0.15 μrad. For the ramp tracking test, the tracking errors are less than 30 nm and 0.3 μrad. For the circular tracking test, the tracking error is less than 55 nm for both X- and Y-axis.

Copyright © 2010 by ASME

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