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Correction: Kang et al. Fluid Flow to Electricity: Capturing Flow-Induced Vibrations with Micro-Electromechanical-System-Based Piezoelectric Energy Harvester. <i>Micromachines</i> 2024, <i>15</i>, 581.

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Optimal Controller Design for Ultra-Precision Fast-Actuation Cutting Systems.

Fei Ding1, Xichun Luo2, Duo Li1

  • 1Centre for Precision Engineering, Harbin Institute of Technology, Harbin 150001, China.

Micromachines
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

A new deterministic controller design minimizes positioning errors in fast-actuation systems for optical machining. This approach improves precision, reducing root-mean-square (RMS) positioning error from 1.19 nm to 0.68 nm.

Keywords:
controller tuningfast tool servomicrostructureoptimal controlultra-precision machining

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Area of Science:

  • Precision Engineering
  • Optical Metrology
  • Control Systems

Background:

  • Fast-actuation cutting systems are crucial for freeform optical part machining.
  • Current controller tuning relies on experience, hindering performance prediction during design.

Purpose of the Study:

  • To develop a deterministic controller design approach for fast-actuation systems.
  • To quantitatively analyze the impact of mechanical parameters on positioning stability.

Main Methods:

  • Implemented a deterministic control law minimizing positioning errors using plant and disturbance models.
  • Analyzed the closed-loop system's response to mechanical parameters like mass, damping, and stiffness.
  • Determined optimal bearing stiffness and damping coefficients under measured disturbance conditions.

Main Results:

  • Reduced root-mean-square (RMS) positioning error from 1.19 nm to 0.68 nm.
  • Identified optimal bearing stiffness (1.1×10^5 N/m) and damping (237.7 N/(m·s^-1)).
  • Demonstrated that increased moving inertia effectively reduces high-frequency disturbances.

Conclusions:

  • The deterministic controller design enhances positioning accuracy in optical machining systems.
  • Provides a quantitative understanding of how structural parameters influence positioning stability.
  • Offers insights for error reduction from a design perspective.