Design and Control of a Portable AO System

Abstract

In the field of Adaptive Optics (AO) a new development has been introduced recently: the Wavefront Sensorless Adaptive Optics (WFSless AO) approach, which only uses camera measurements to optimize image quality and does not use a Shack-Hartman (SH) Wavefront Sensor (WFS) [Booth, 2006, Débarre et al., 2009, Hinnen et al., 2008]. In order to optimize the image quality of a point source or an extended object, the residual wavefront should be minimised as much as possible. The Model Based Approach [Booth, 2007, Linhai and Rao, 2011] is a very promising approach which can be used for this optimisation, resulting in a significant reduction in the amount of measurements. This approach uses an initial measurement and then excites all the modes that need to be optimised separately and takes a measurement of the intensity distribution after each excitation. Then, the Second-Moment (SM)’s of the measured intensity distributions are computed and used for the computation of the optimal control input, which is the coefficient vector in a Zonal representation of the wavefront. The control vector can be used to minimise the residual wavefront and thus optimise the image quality. However, in this approach it is assumed that the incoming wavefront does not change between the first and last measurement. This assumption is called the frozen window time and it is not valid for real-time problems. In [Lianghua et al., 2017] a solution was proposed for this problem, by using a precomputed independent set of modes, which allows a decoupling of the modes and computations and reduces the frozen window time. Unfortunately, this research introduces more measurements which leads to more delay. In this thesis a new approach will be presented, the Continuous Model Based Approach. This approach tries to solve both problems simultaneously: a reduction of the number of measurements and a reduction of the frozen window time. The number of measurements will be reduced by combining the excitation and optimisation step and the frozen window time will be reduced by using a two step optimisation, based on [Lianghua et al., 2017]. First, the validity of this approach will be proven mathematically and with simulations, then it will be tested in experiments. In order to make the step to real-time experiments, also an online calibration method will be introduced, proven and tested. This is also a new technique, based on the Model Based Approach [Linhai and Rao, 2011] and can be used to obtain the correlation matrix for any set of modes, focussed on actuator responses.