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A Protocol for Real-time 3D Single Particle Tracking

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An adaptive recurrent radial basis function network tracking controller for a two-dimensional piezo-positioning

Faa-Jeng Lin¹, Hsin-Jang Shieh, Po-Kai Huang

¹Department of Electrical Engineering, Nat. Central Univ., Chungli, Taiwan. linfj@ee.ncu.edu.tw

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

|March 13, 2008

Summary

This summary is machine-generated.

This study introduces an adaptive recurrent radial basis function network (ARRBFN) tracking controller for 2D piezo-positioning stages. The ARRBFN controller significantly enhances position tracking performance and robustness against uncertainties like hysteresis and cross-coupling.

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Area of Science:

Robotics and Control Systems
Mechatronics Engineering
Applied Mathematics

Background:

Piezo-positioning stages are crucial for high-precision applications but are affected by complex dynamics.
Modeling these dynamics requires accounting for hysteresis friction and cross-coupling stiffness.
Existing controllers may struggle with the uncertainties inherent in these systems.

Purpose of the Study:

To propose a novel adaptive recurrent radial basis function network (ARRBFN) tracking controller.
To develop a robust control strategy for two-dimensional (2D) piezo-positioning stages.
To improve position tracking accuracy and system robustness against dynamic uncertainties.

Main Methods:

Development of a mathematical model incorporating hysteresis friction and non-constant stiffness with cross-coupling.
Design of an ARRBFN controller utilizing a recurrent radial basis function network (RRBFN) for unknown dynamic function approximation.
Derivation of adaptive learning algorithms using Lyapunov stability theorem and a robust compensator with adaptive uncertainty estimation.

Main Results:

The proposed ARRBFN control scheme substantially improves position tracking performance.
The controller demonstrates robustness against uncertainties, including hysteresis friction and cross-coupling stiffness.
Experimental results validate the enhanced tracking performance and robustness to external loads.

Conclusions:

The adaptive recurrent radial basis function network (ARRBFN) tracking controller offers superior performance for 2D piezo-positioning stages.
The controller effectively manages complex dynamics such as hysteresis and cross-coupling.
The study confirms the ARRBFN's capability in achieving high-precision positioning with robustness.