Adaptive gain in closed-loop tilt control and adaptive optics

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.