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A Morphing Point-to-Point Displacement Control Based on Long Short-Term Memory for a Coplanar XXY Stage.

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This study introduces a deep learning approach using Long Short-Term Memory (LSTM) to enhance precision for XXY stages. The LSTM model significantly reduced positioning errors and settling time compared to traditional charge-coupled device (CCD) imaging systems.

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Area of Science:

  • Precision Engineering
  • Robotics
  • Machine Learning

Background:

  • Traditional visual recognition systems using charge-coupled device (CCD) imaging for stage control face limitations in accuracy and speed.
  • Image resolution constraints in CCD systems lead to significant positioning errors and long settling times.

Purpose of the Study:

  • To develop and evaluate a Long Short-Term Memory (LSTM) deep learning model for improving the positioning accuracy and reducing the settling time of an XXY stage.
  • To compare the performance of an LSTM-based feedback control system against a conventional CCD imaging system for precise stage motion control.

Main Methods:

  • A coplanar XXY stage's movement was initially controlled using a CCD image feedback system.
  • A Long Short-Term Memory (LSTM) deep learning model was trained using position data, initially aided by a dial indicator, to learn stage motion dynamics.
  • An LSTM-based feedback control system was implemented to control the XXY stage without external assistance.

Main Results:

  • The conventional CCD system resulted in an average positioning error of 6.712 µm and a settling time of approximately 7 s.
  • The LSTM-based system achieved a significantly lower average positioning error of 2.085 µm and a reduced settling time of 2.02 s.
  • The LSTM model demonstrated superior performance across key positioning indices, including average error, root mean square error, and settling time.

Conclusions:

  • Long Short-Term Memory (LSTM) deep learning models offer a substantial advancement in precision stage control.
  • LSTM-based feedback systems provide higher control accuracy and faster response times compared to traditional CCD imaging methods.
  • This approach holds promise for applications requiring highly precise and efficient motion control.