Data-driven Estimation, Tracking, and System Identification of Deterministic and Stochastic Optical Spot Dynamics

TL;DR

This paper presents a unified data-driven framework for modeling, estimating, and controlling optical spot disturbances using covariance estimation, optimization, and subspace identification, validated through laboratory experiments.

Contribution

It introduces a novel, experimentally verified data-driven approach for optical-spot disturbance modeling and Kalman filter tuning, integrating spectral factorization and subspace methods.

Findings

Effective disturbance modeling with spectral factorization

Improved Kalman filter covariance tuning

Successful experimental validation in optical setup

Abstract

Stabilization, disturbance rejection, and control of optical beams and optical spots are ubiquitous problems that are crucial for the development of optical systems for ground and space telescopes, free-space optical communication terminals, precise beam steering systems, and other types of optical systems. High-performance disturbance rejection and control of optical spots require the development of disturbance estimation and data-driven Kalman filter methods. Motivated by this, we propose a unified and experimentally verified data-driven framework for optical-spot disturbance modeling and tuning of covariance matrices of Kalman filters. Our approach is based on covariance estimation, nonlinear optimization, and subspace identification methods. Also, we use spectral factorization methods to emulate optical-spot disturbances with a desired power spectral density in an optical laboratory environment. We test the effectiveness of the proposed approaches on an experimental setup consisting of a piezo tip-tilt mirror, piezo linear actuator, and a CMOS camera.