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The Scientific World Journal
Volume 2013, Article ID 641269, 12 pages
Research Article

A Quasiphysics Intelligent Model for a Long Range Fast Tool Servo

1College of Mechanical Science and Engineering, Jilin University, Changchun 130022, China
2College of Electromechanical Engineering, Changchun University of Technology, Changchun 130012, China

Received 18 July 2013; Accepted 13 August 2013

Academic Editors: C. Bao and A. Szekrenyes

Copyright © 2013 Qiang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Accurately modeling the dynamic behaviors of fast tool servo (FTS) is one of the key issues in the ultraprecision positioning of the cutting tool. Herein, a quasiphysics intelligent model (QPIM) integrating a linear physics model (LPM) and a radial basis function (RBF) based neural model (NM) is developed to accurately describe the dynamic behaviors of a voice coil motor (VCM) actuated long range fast tool servo (LFTS). To identify the parameters of the LPM, a novel Opposition-based Self-adaptive Replacement Differential Evolution (OSaRDE) algorithm is proposed which has been proved to have a faster convergence mechanism without compromising with the quality of solution and outperform than similar evolution algorithms taken for consideration. The modeling errors of the LPM and the QPIM are investigated by experiments. The modeling error of the LPM presents an obvious trend component which is about ±1.15% of the full span range verifying the efficiency of the proposed OSaRDE algorithm for system identification. As for the QPIM, the trend component in the residual error of LPM can be well suppressed, and the error of the QPIM maintains noise level. All the results verify the efficiency and superiority of the proposed modeling and identification approaches.