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Robust iterative learning contouring controller with disturbance observer for machine tool feed drives.

Kenneth Renny Simba1, Ba Dinh Bui2, Mathew Renny Msukwa3

  • 1Department of Mechanical Engineering, Toyohashi University of Technology, Toyohashi, Aichi, 441-8580, Japan; Directorate of Nuclear Technology, Tanzania Atomic Energy Commission, Arusha, Tanzania.

ISA Transactions
|February 25, 2018
PubMed
Summary

This study introduces a new iterative learning contouring controller to reduce contour errors in machine tool feed drive systems. The proposed robust controller significantly enhances precision for manufacturing complex parts.

Keywords:
Computer numerical control machinesController designIterative learning controlMachine learningMachine toolsNonlinear control systemsNonlinear friction compensator

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Area of Science:

  • Control Systems Engineering
  • Manufacturing Technology
  • Robotics and Automation

Background:

  • Contour errors in feed drive systems are critical for manufacturing precision, exceeding individual axial tracking errors.
  • Machining complex shapes or sharp corners often results in significant contour errors due to trajectory discontinuities and nonlinear uncertainties.
  • Robust controllers are essential for improving the tracking performance of feed drive systems in precision manufacturing.

Purpose of the Study:

  • To propose an iterative learning contouring controller designed to minimize contour errors in feed drive systems.
  • To enhance the tracking accuracy of machine tools, particularly when manufacturing products with complex geometries.
  • To address the challenges posed by discontinuous trajectories and nonlinear uncertainties in high-precision machining.

Main Methods:

  • Development of an iterative learning contouring controller integrating a Proportional-Derivative (PD) controller.
  • Incorporation of a disturbance observer to compensate for system uncertainties and improve robustness.
  • Experimental evaluation using a sharp-corner trajectory to compare performance against conventional controllers.

Main Results:

  • The proposed controller demonstrated a significant reduction in contour errors.
  • Experimental results showed an average reduction of approximately 37% in maximum contour error.
  • The controller effectively improved tracking performance on a challenging sharp-corner trajectory.

Conclusions:

  • The iterative learning contouring controller with a disturbance observer is effective in reducing contour errors in feed drive systems.
  • The proposed method offers a viable solution for enhancing precision in manufacturing, especially for complex part geometries.
  • This approach provides a robust control strategy for machine tools facing trajectory discontinuities and nonlinearities.