Position Estimator Design for a MEMS Top-Drive Electrostatic Rotary Actuator.

Area of Science:

• * Microelectromechanical Systems (MEMS)
• * Actuator technology
• * Sensor signal processing

Background:

• * MEMS top-drive electrostatic rotary actuators exhibit nonlinear capacitance-rotor angle relationships.
• * Non-linearities arise from fringe effects and low aspect ratio electrodes, complicating position estimation.
• * Traditional linear models are insufficient for accurate sensing in these systems.

Purpose of the Study:

• * To develop a method for accurate rotor angle estimation in MEMS rotary actuators.
• * To address the challenges posed by nonlinear capacitance and combined actuation/sensing requirements.
• * To improve the precision and stability of position sensing in electrostatic rotary actuators.

Main Methods:

• * Derivation of a data-driven nonlinear capacitance model.
• * Separation of stator parts for actuation and sensing to enable accurate capacitance measurement.
• * Design of an unscented Kalman filter utilizing the nonlinear model and capacitance data.

Main Results:

• * The data-driven nonlinear model accurately represents the actuator's capacitance behavior.
• * The unscented Kalman filter effectively mitigates estimation errors caused by periodic nonlinearity.
• * Stable and accurate rotor angle estimation was achieved, surpassing traditional methods.

Conclusions:

• * The proposed approach enables precise position estimation in MEMS rotary actuators with nonlinear capacitance.
• * The method of combining a nonlinear model with an unscented Kalman filter is effective for similar systems.
• * This work advances sensing capabilities for electrostatic rotary actuators.