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Modeling, Identification, and Adaptive Robust Motion Control of Voice-Coil Motor Driven Stages

[+] Author Affiliations
Chao Li, Xiaocong Zhu, Hao Liu

Zhejiang University, Hangzhou, China

Zheng Chen

Zhejiang University, Hangzhou, ChinaDalhousie University, Halifax, NS, Canada

Bin Yao

Zhejiang University, Hangzhou, ChinaPurdue University, West Lafayette, IN

Paper No. DSCC2013-3784, pp. V001T14A002; 8 pages
  • ASME 2013 Dynamic Systems and Control Conference
  • Volume 1: Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications; Bio-Medical and Bio-Mechanical Systems; Biomedical Robots and Rehab; Bipeds and Locomotion; Control Design Methods for Adv. Powertrain Systems and Components; Control of Adv. Combustion Engines, Building Energy Systems, Mechanical Systems; Control, Monitoring, and Energy Harvesting of Vibratory Systems
  • Palo Alto, California, USA, October 21–23, 2013
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5612-3
  • Copyright © 2013 by ASME


Voice-coil motors are widely used in precision motion control of industrial applications such as head positioning of hard disk drives, semiconductor fabrication and packaging. In this paper, to achieve high precision movement potential of vertical voice-coil motor driven stages, the accurate modeling of nonlinear rigid body dynamics is developed, and the model parameters are estimated by the identification experiments in time domain. The neglected high-frequency dynamics are also identified through frequency response experiments to verify the validity of the frequency range of the proposed rigid-body dynamical model. To attenuate the serious nonlinear effect of the plant dynamics, Coulomb friction compensation is used when obtaining the frequency response results. Based on the verified nonlinear rigid-body dynamical model, an adaptive robust controller is developed to obtain a guaranteed performance in the presence of both parametric uncertainties and uncertain nonlinearities. Comparative control experimental results obtained show the effectiveness and good tracking performance of the proposed ARC algorithm.

Copyright © 2013 by ASME



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