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Note: Precision control of nano-positioning stage: An iterative learning-based model predictive control approach.

Shengwen Xie1, Juan Ren1

  • 1Department of Mechanical Engineering, Iowa State University, Ames, Iowa 50011, USA.

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Precise nano-positioning is challenging due to nonlinearities. An iterative learning-based model predictive control (IL-MPC) approach enhances trajectory tracking accuracy for high-speed applications.

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Area of Science:

  • Control Systems Engineering
  • Mechatronics
  • Nanotechnology

Background:

  • Nano-positioning stages face challenges in precise high-speed trajectory tracking due to mechanical vibrations and nonlinearities like hysteresis and creep.
  • Existing control methods struggle to overcome these limitations, hindering performance in demanding applications.

Purpose of the Study:

  • To introduce and validate an Iterative Learning-based Model Predictive Control (IL-MPC) approach for precise nano-positioning.
  • To address the difficulties in achieving high-speed/bandwidth trajectory tracking in the presence of system nonlinearities and uncertainties.

Main Methods:

  • The proposed IL-MPC combines Model Predictive Control (MPC) for tracking error limitation across iterations and Iterative Learning Control (ILC) for enhanced precision.
  • ILC is applied to the system comprising the MPC and the nano-positioning stage to compensate for modeling uncertainties and nonlinearities.

Main Results:

  • Experimental validation demonstrated the effectiveness of IL-MPC on a nano-piezo actuator.
  • The approach successfully achieved high-precision tracking for both repetitive high-speed/broadband trajectories and trajectories with dynamic variations in amplitude, phase, and frequency.

Conclusions:

  • The IL-MPC approach offers a robust solution for precise trajectory tracking in nano-positioning systems.
  • This method effectively mitigates the impact of nonlinearities and uncertainties, paving the way for improved performance in advanced mechatronic systems.