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Video-rate Scanning Confocal Microscopy and Microendoscopy
Published on: October 20, 2011
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Motion Estimation for a Compact Electrostatic Microscanner via Shared Driving and Sensing Electrodes in
Yi Chen1, Miki Lee2, Mayur Bhushan Birla3
1University of Michigan, Ann Arbor, MI 48109, USA. He is currently with Midea ETC, 250 W Tasman Dr, Suite 190, San Jose, CA 95134, USA.
IEEE/ASME Transactions on Mechatronics : a Joint Publication of the IEEE Industrial Electronics Society and the ASME Dynamic Systems and Control Division
|January 27, 2021
Summary
This study introduces a novel method for accurately estimating high-frequency rotary motion in compact electrostatic micro-scanners. The technique significantly improves motion estimation accuracy, crucial for advanced imaging applications.
Area of Science:
- Micro-electro-mechanical systems (MEMS)
- Robotics and Control Systems
- Signal Processing
Background:
- Accurate estimation of high-frequency rotary motion is vital for micro-scanner applications like image reconstruction.
- Compact electrostatic micro-scanners face challenges in motion sensing due to size constraints limiting dedicated sensing electrodes.
- Environmental factors like temperature can alter micro-scanner dynamics, impacting motion estimation accuracy.
Purpose of the Study:
- To develop a method for estimating high-frequency rotary motion in compact electrostatic micro-scanners using shared actuation and sensing electrodes.
- To address the challenge of limited sensing capabilities in miniaturized devices.
- To improve the accuracy of rotary motion estimation for enhanced performance in image reconstruction.
Main Methods:
- Utilized electromechanical amplitude modulation (EAM) to differentiate motion signals from parasitic capacitance feedthrough.
- Derived a novel non-linear measurement model linking large out-of-plane angular motion to circuit output.
- Implemented extended Kalman filter (EKF) and unscented Kalman filter (UKF) with a process model based on parametric resonant dynamics and the derived measurement model.
Main Results:
- The proposed method significantly improved micro-scanner rotary motion estimation accuracy compared to methods without the measurement model.
- Achieved an 86.1% reduction in root-mean-square error (RMSE) for phase shift estimation.
- Demonstrated a 78.5% reduction in RMSE for angular position error.
Conclusions:
- The developed method effectively estimates high-frequency rotary motion in compact electrostatic micro-scanners, even with integrated actuation and sensing electrodes.
- The novel non-linear measurement model and Kalman filtering approach enhance accuracy and overcome sensing limitations.
- This advancement holds promise for improving the performance of micro-scanner-based imaging systems.
