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Hybrid approach for deformable mirror online system identification using RLS algorithm and adaptive forgetting factor

M A Aghababayee, M Mosayebi, H Saghafifar

|November 22, 2024

Summary

This study introduces an adaptive forgetting factor recursive least squares (AFFRLS) algorithm for online system identification of deformable mirrors. The AFFRLS method demonstrates high accuracy and noise reduction for dynamic behavior analysis.

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Area of Science:

Engineering
Control Systems
Signal Processing

Background:

Deformable mirrors (DM) are critical components in adaptive optics systems.
Accurate dynamic modeling of DMs is essential for precise control.
Existing system identification methods may face challenges with real-time applications and noise.

Purpose of the Study:

To propose and validate an online system identification approach for deformable mirror dynamics.
To introduce the adaptive forgetting factor recursive least squares (AFFRLS) algorithm for this purpose.
To evaluate the accuracy, simplicity, and noise reduction capabilities of the AFFRLS method.

Main Methods:

Utilized COMSOL multi-physics software for finite element modeling of the DM.
Integrated COMSOL with MATLAB via Livelink for data transfer.
Implemented and optimized the AFFRLS algorithm for online system identification.
Validated the model by comparing its output with new input signals.

Main Results:

The AFFRLS algorithm successfully identified the dynamic behavior of the deformable mirror with acceptable accuracy.
The proposed method showed significant advantages in accuracy, simplicity, and noise reduction.
A slight decrease in speed was observed due to computational load.

Conclusions:

The AFFRLS algorithm is a novel and effective approach for online system identification of deformable mirrors.
This method offers a robust solution for dynamic behavior analysis, outperforming other techniques in key metrics.
Further optimization may be needed to address computational speed limitations.