Latency in the control loop of adaptive optics ( AO ) systems can severely limit performance .
Under the frozen flow hypothesis linear predictive control techniques can overcome this , however identification and tracking of relevant turbulent parameters ( such as wind speeds ) is required for such parametric techniques .
This can complicate practical implementations and introduce stability issues when encountering variable conditions .
Here we present a nonlinear wavefront predictor using a Long Short-Term Memory ( LSTM ) artificial neural network ( ANN ) that assumes no prior knowledge of the atmosphere and thus requires no user input .
The ANN is designed to predict the open-loop wavefront slope measurements of a Shack-Hartmann wavefront sensor ( SH-WFS ) one frame in advance to compensate for a single-frame delay in a simulated 7 \times 7 single-conjugate adaptive optics ( SCAO ) system operating at 150 Hz .
We describe how the training regime of the LSTM ANN affects prediction performance and show how the performance of the predictor varies under various guide star magnitudes .
We show that the prediction remains stable when both wind speed and direction are varying .
We then extend our approach to a more realistic two-frame latency system .
AO system performance when using the LSTM predictor is enhanced for all simulated conditions with prediction errors within 19.9 to 40.0 nm RMS of a latency-free system operating under the same conditions compared to a bandwidth error of 78.3 \pm 4.4 nm RMS .