  We incorporate deep learning (DL) into coherent beam combining (CBC) systems
for the first time, to the best of our knowledge. Using a well-trained
convolutional neural network DL model, the phase error in CBC systems could be
accurately estimated and preliminarily compensated. Then, the residual phase
error is further compensated by stochastic parallel gradient descent (SPGD)
algorithms. The two-stage phase control strategy combined with DL and SPGD
algorithms is validated to be a feasible and promising technique to alleviate
the long-standing problem that the phase control bandwidth decreases as the
number of array elements expands. Further investigation denotes that the
proposed phase control technique could be employed to generate orbital angular
momentum (OAM) beams with different orders by distinguishing the OAM beams of
conjugated phase distributions.
