Abstract
Time lag between wavefront detection and correction in Adaptive Optics (AO) systems can sometimes severely degrade its targeted performance. We propose a nonlinear predictor based on long short-term memory (LSTM) to predict open-loop wavefronts in the next time step based on a time series of past measurements. Compared with linear predictive control technique, this approach is inherently model free. Incorporation of LSTMs offer additional benefit of self tuning, which is especially favourable in terms of evolving turbulence. Numerical simulations based on a low-order single-conjugate AO (SCAO) system demonstrate over 50% reduction in bandwidth error in a relatively wide range of application scenarios. Agility and robustness against non-stationary turbulence is also demonstrated using time-variant wind profile.
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Liu, X., Morris, T., Saunter, C. (2019). Using Long Short-Term Memory for Wavefront Prediction in Adaptive Optics. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_43
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