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Multivariable Iterative Learning Control Design for Precision Control of Flexible Feed Drives.

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  • 1Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.

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Summary

This study introduces a multivariable iterative learning control (MILC) for CNC machine tools. The method improves dynamic positioning accuracy by using motor and table feedback, enhancing precision in flexible feed drive systems.

Keywords:
flexible feed drivesiterative learning controlmultivariable controlnorm-optimal

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Area of Science:

  • Mechanical Engineering
  • Control Systems Engineering

Background:

  • Modern machining requires higher speeds and precision, driving demand for advanced control systems in CNC machine tools.
  • Conventional CNC controllers use motor-side rotary encoders for position feedback to ensure stability and prevent component damage, but limitations remain.
  • Factors like lead errors, vibrations, and thermal deformation impact positioning accuracy in flexible feed drive systems.

Purpose of the Study:

  • To develop and validate a novel multivariable iterative learning control (MILC) method for flexible CNC feed drive systems.
  • To enhance dynamic positioning accuracy by integrating feedback from both motor and table sides.
  • To mitigate control conflicts and reduce tracking errors in flexible structures.

Main Methods:

  • Implementation of a multivariable iterative learning control (MILC) strategy.
  • Utilization of error data from both motor and table sides for enhanced precision.
  • Injection of compensation commands into reference trajectory and control command via norm-optimization.
  • Experimental validation on an industrial biaxial CNC machine tool.

Main Results:

  • The MILC method effectively enhances dynamic positioning accuracy in flexible feed drive systems.
  • Integration of dual-side feedback (motor and table) leads to superior precision.
  • Conflicts between feedback control (FBC) and traditional iterative learning control (ILC) are mitigated.
  • Significantly smaller tracking errors were achieved on the table side.

Conclusions:

  • The proposed MILC method offers a robust solution for improving precision in CNC machine tools.
  • This approach demonstrates significant potential for advancing control strategies in high-speed, high-precision machining.
  • Experimental validation confirms the efficacy of MILC for industrial applications.