  We classified the decoupled stochastic parallel gradient descent (SPGD)
optimization model into two different types: software and hardware decoupling
methods. A kind of software decoupling method is then proposed and a kind of
hardware decoupling method is also proposed depending on the Shack-Hartmann
(S-H) sensor. Using the normal sensor to accelerate the convergence of
algorithm, the hardware decoupling method seems a capable realization of
decoupled method. Based on the numerical simulation for correction of phase
distortion in atmospheric turbulence, our methods are analyzed and compared
with basic SPGD model and also other decoupling models, on the aspects of
different spatial resolutions, mismatched control channels and noise. The
results show that the phase distortion can be compensated after tens iterations
with a strong capacity of noise tolerance in our model.
