10 July 2018 Adaptive gain in closed-loop tilt control and adaptive optics
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Abstract
The performance of closed-loop tilt-control and adaptive-optics systems suffers when conditions change. Examples of changing conditions are angular extent of the object, signal-to-noise ratio, and characteristics of the disturbance. A simple learning algorithm motivated by neural network theory is developed to change the closed-loop gain in real-time to adapt quickly to changing conditions. This technique finds the correct loop gain within seconds with no operator intervention, which saves several minutes for each observation. Simulation and experimental results show improvement for both tilt-control and adaptive-optics systems.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dennis A. Montera, Dennis A. Montera, James M. Brown, James M. Brown, Odell R. Reynolds, Odell R. Reynolds, Miles D. Buckman, Miles D. Buckman, Darryl J. Sanchez, Darryl J. Sanchez, Denis W. Oesch, Denis W. Oesch, Erica M. Hoeffner, Erica M. Hoeffner, Michael W. Bishop, Michael W. Bishop, Brian T. Kay, Brian T. Kay, Tyler J. Hardy, Tyler J. Hardy, "Adaptive gain in closed-loop tilt control and adaptive optics", Proc. SPIE 10703, Adaptive Optics Systems VI, 107031H (10 July 2018); doi: 10.1117/12.2310193; https://doi.org/10.1117/12.2310193
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