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Data-driven estimation, tracking, and system identification of deterministic and stochastic optical spot dynamics
Optics Express
|June 29, 2023
Summary
This study introduces a data-driven framework for modeling optical spot disturbances and tuning Kalman filters, crucial for stable optical systems. The method enhances disturbance rejection and control in applications like telescopes and optical communication.
Area of Science:
- Optics and Photonics
- Control Systems Engineering
- Signal Processing
Background:
- Stabilization and control of optical beams/spots are critical for advanced optical systems.
- High-performance disturbance rejection necessitates advanced estimation and data-driven Kalman filter methods.
Purpose of the Study:
- To propose a unified, experimentally verified data-driven framework for optical-spot disturbance modeling.
- To enable data-driven tuning of Kalman filter covariance matrices for improved optical control.
Main Methods:
- Covariance estimation, nonlinear optimization, and subspace identification were employed.
- Spectral factorization methods were used to emulate optical disturbances in a lab setting.
- A data-driven framework was developed and experimentally validated.
Main Results:
- The proposed framework effectively models optical-spot disturbances.
- The approach allows for data-driven tuning of Kalman filter parameters.
- Experimental validation demonstrated the framework's effectiveness on a piezo-actuated optical setup.
Conclusions:
- The developed data-driven framework provides a unified approach to optical disturbance modeling and Kalman filter tuning.
- This method is essential for advancing control and stabilization in complex optical systems.
- The findings have implications for ground/space telescopes, optical communication, and beam steering systems.
