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A quasiphysics intelligent model for a long range fast tool servo.

Qiang Liu1, Xiaoqin Zhou, Jieqiong Lin

  • 1College of Mechanical Science and Engineering, Jilin University, Changchun 130022, China.

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|October 29, 2013
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A new quasiphysics intelligent model (QPIM) accurately describes fast tool servo (FTS) dynamics. An Opposition-based Self-adaptive Replacement Differential Evolution (OSaRDE) algorithm enhances parameter identification for ultraprecision positioning systems.

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Area of Science:

  • Mechanical Engineering
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Accurate modeling of fast tool servo (FTS) dynamics is crucial for ultraprecision positioning.
  • Voice coil motor (VCM) actuated long-range FTS (LFTS) present complex dynamic behaviors.

Purpose of the Study:

  • To develop a quasiphysics intelligent model (QPIM) for LFTS dynamic behavior.
  • To propose a novel Opposition-based Self-adaptive Replacement Differential Evolution (OSaRDE) algorithm for parameter identification.

Main Methods:

  • Integration of a linear physics model (LPM) with a radial basis function (RBF) neural network (NM) to form the QPIM.
  • Application of the OSaRDE algorithm for efficient and accurate parameter identification of the LPM.
  • Experimental validation of LPM and QPIM modeling errors.

Main Results:

  • The OSaRDE algorithm demonstrated faster convergence and superior performance compared to other evolutionary algorithms.
  • The LPM exhibited a modeling error trend of ±1.15%, validating the OSaRDE algorithm's effectiveness.
  • The QPIM successfully suppressed the trend component in the LPM's residual error, achieving noise-level accuracy.

Conclusions:

  • The proposed QPIM provides a superior approach for modeling LFTS dynamic behaviors.
  • The OSaRDE algorithm is an efficient and effective tool for system identification in precision engineering.
  • The combined modeling and identification strategy enhances the performance of ultraprecision positioning systems.