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Tracking error reduction in CNC machining by reshaping the kinematic trajectory

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Abstract

In this paper, a method of reducing the tracking error in CNC machining is proposed. The structured neural network is used to approximate the discontinuous friction in CNC machining, which has jump points and uncertainties. With the estimated nonlinear friction function, the reshaped trajectory can be computed from the desired one by solving a second order ODE such that when the reshaped trajectory is fed into the CNC controller, the output is the desired trajectory and the tracking error is eliminated in certain sense. The proposed reshape method is also shown to be robust with respect to certain parameters of the dynamic system.

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Authors and Affiliations

  1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Jianxin Guo & Xiao-Shan Gao

  2. College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, 266580, China

    Qiang Zhang

Authors
  1. Jianxin Guo
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  2. Qiang Zhang
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  3. Xiao-Shan Gao
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Corresponding author

Correspondence to Jianxin Guo.

Additional information

This paper is partially supported by a National Key Basic Research Project of China, by a USA NSF grant CCR-0201253, and by the Foundation of UPC for the Author of National Excellent Doctoral Dissertation under Grant No. 120501A.

This paper was recommended for publication by Guest Editor LI Hongbo.

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Guo, J., Zhang, Q. & Gao, XS. Tracking error reduction in CNC machining by reshaping the kinematic trajectory. J Syst Sci Complex 26, 817–835 (2013). https://doi.org/10.1007/s11424-013-3179-x

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  • DOI: https://doi.org/10.1007/s11424-013-3179-x

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