Subspace identification of low-dimensional Structural-Thermal-Optical-Performance (STOP) models of reflective optics

TL;DR

This paper demonstrates that subspace system identification can accurately estimate low-dimensional models of complex, large-scale Structural-Thermal-Optical-Performance dynamics in reflective optics, enabling better prediction and control.

Contribution

It introduces a novel application of subspace identification to large-dimensional STOP models, showing they can be approximated by low-dimensional state-space models.

Findings

Low-dimensional models accurately represent large-scale STOP dynamics.

The approach enables effective prediction and control of thermal-induced wavefront aberrations.

Codes for modeling and estimation are publicly available.

Abstract

In this paper, we investigate the feasibility of using subspace system identification techniques for estimating transient Structural-Thermal-Optical Performance (STOP) models of reflective optics. As a test case, we use a Newtonian telescope structure. This work is motivated by the need for the development of model-based data-driven techniques for prediction, estimation, and control of thermal effects and thermally-induced wavefront aberrations in optical systems, such as ground and space telescopes, optical instruments operating in harsh environments, optical lithography machines, and optical components of high-power laser systems. We estimate and validate a state-space model of a transient STOP dynamics. First, we model the system in COMSOL Multiphysics. Then, we use LiveLink for MATLAB software module to export the wavefront aberrations data from COMSOL to MATLAB. This data is used to test the subspace identification method that is implemented in Python. One of the main challenges in modeling and estimation of STOP models is that they are inherently large-dimensional. The large-scale nature of STOP models originates from the coupling of optical, thermal, and structural phenomena and physical processes. Our results show that large-dimensional STOP dynamics of the considered optical system can be accurately estimated by low-dimensional state-space models. Due to their low-dimensional nature and state-space forms, these models can effectively be used for the prediction, estimation, and control of thermally-induced wavefront aberrations. The developed MATLAB, COMSOL, and Python codes are available online.