Modeling and state-space identification of deformable mirrors

被引:24
|
作者
Haber, Aleksandar [1 ]
Verhaegen, Michel [2 ]
机构
[1] CUNY Coll Staten Isl, Dept Engn & Environm Sci, 2800 Victory Blvd, Staten Isl, NY 10314 USA
[2] Delft Univ Technol, Delft Ctr Syst & Control, Mekelweg 5, NL-2628 CD Delft, Netherlands
来源
OPTICS EXPRESS | 2020年 / 28卷 / 04期
基金
欧盟地平线“2020”;
关键词
ADAPTIVE SECONDARY MIRROR; PREDICTIVE CONTROL; SHAPE CONTROL; DESIGN; CONTROLLER;
D O I
10.1364/OE.382880
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
To develop high-performance controllers for adaptive optics (AO) systems, it is essential to first derive sufficiently accurate state-space models of deformable mirrors (DMs). However, it is often challenging to develop realistic large-scale finite element (FE) state-space models that take into account the system damping, actuator dynamics, boundary conditions, and multi-physics phenomena affecting the system dynamics. Furthermore, it is challenging to establish a modeling framework capable of the automated and quick derivation of state-space models for different actuator configurations and system geometries. On the other hand, for accurate model-based control and system monitoring, it is often necessary to estimate state-space models from the experimental data. However, this is a challenging problem since the DM dynamics is inherently infinite-dimensional and it is characterized by a large number of eigenmodes and eigenfrequencies. In this paper, we provide modeling and estimation frameworks that address these challenges. We develop an FE state-space model of a faceplate DM that incorporates damping and actuator dynamics. We investigate the frequency and time domain responses for different model parameters. The state-space modeling process is completely automated using the LiveLink for MATLAB toolbox that is incorporated into the COMSOL Multiphysics software package. The developed state-space model is used to generate the estimation data. This data, together with a subspace identification algorithm, is used to estimate reduced-order DM models. We address the model-order selection and model validation problems. The results of this paper provide essential modeling and estimation tools to broad AO and mechatronics scientific communities. The developed Python, MATLAB, and COMSOL Multiphysics codes are available online. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
引用
收藏
页码:4726 / 4740
页数:15
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