By Topic

Geometrical Error Modeling and Compensation Using Neural Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
K. K. Tan ; Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore ; S. N. Huang ; S. Y. Lim ; Y. P. Leow
more authors

This paper describes an approach based on neural networks (NNs) for geometrical error modeling and compensation for precision motion systems. A laser interferometer is used to obtain the systematic error measurements of the geometrical errors, based on which an error model may be constructed and, consequently, a model-based compensation may be incorporated in the motion-control system. NNs are used to approximate the components of geometrical errors, thus dispensing with the conventional lookup table. Apart from serving as a more adequate model due to its inherent nonlinear characteristics, the use of NNs also results in less memory requirements to implement the error compensation for a specified precision compared to the use of lookup table. The adequacy and clear benefits of the proposed approach are illustrated via applications to various configurations of precision-positioning stages, including a single-axis, a gantry, and a complete XY stage

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)  (Volume:36 ,  Issue: 6 )