Download or read book Distributed Control of a Retinal Imaging Adaptive Optics System written by Maurizio Ficocelli and published by . This book was released on 2008 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years a technique known as adaptive optics (AO), commonly used to correct the dynamic wavefront error caused by atmospheric turbulence in ground-based telescopes, has been studied as a means of correcting the dynamic wavefront error caused by aberrations in the human eye, allowing for higher resolution retinal images. In order to compensate for these dynamic aberrations, a compact AO system that tracks and compensates for these changes in real-time is needed. To realize such a system, improvements must be made in two key components, namely the control algorithms and the design of the wavefront corrector. The latter represents the main focus of this thesis. The control problem in adaptive optics systems is generalized to that of shape control for a flexible membrane representing a deformable membrane mirror used in the wavefront corrector. Due to the dynamic nature of the aberrations in the eye, the shape control problem addressed is the tracking of an unknown and time-varying shape for a distributed membrane (i.e., desired shape of the mirror). The proposed controller design approach relies on two steps. The first step is to construct a Q -parameterized set of stabilizing controllers for the system under consideration and to derive conditions on the Q parameter in the controller expression to achieve regulation. Since the desired shape of the mirror is unknown and time-varying, the second step is to derive an online tuning algorithm for the Q parameter in the expression for the parameterized stabilizing controller. The online tuning of the Q parameter allows the controller to converge to the controller needed to achieve regulation, hence compensating for the lack of information on the desired shape for the deformable mirror. Decoupling introduced in the closed loop system allows tuning to be performed using decentralized algorithms.