Current high contrast imaging systems are limited at small angular separations, preventing the direct imaging of Earth-size exoplanets close to their host stars. One primary cause of this is the performance of the extreme adaptive optics (AO) system which is dominated by the servo-lag error at small angular separations. Prediction can be used to help reduce this servo-lag error. Most AO systems today do not use predictive controllers (integral controller), resulting in a phase correction that is proportional to the current measured wavefront and does not take into account the evolution of the wavefront phase uctuations between measurement and correction. To improve upon this, we focus on the development of different predictive control strategies that estimate how the wavefront phase uctuations evolve over a time horizon equal to the servo-lag. Moreover, the statistical properties of the turbulence phase are non-stationary (change in time) which needs to be included in the AO analysis for high contrast imaging systems. First, we show that the non-stationary properties of the atmosphere can be modeled using a von Karman covariance function which can incorporate variations in time of the Fried parameter, outer scale, and wind velocity. Using this new disturbance model of turbulence, the performance of three different predictors-based on a steady state, a recursive, and an adaptive linear-minimum-mean-squareestimator (LMMSE)|is tested under non-stationary conditions. We feed the prediction algorithms wavefront slopes from a 11-by-11 subaperture Shack-Hartmann wavefront sensor. A 97 actuator deformable mirror applies the predictor's phase correction. We present the latest results of our simulations and compare our predictors with the common integrator. We show that under our varying wind conditions, the root-mean-square wavefront error can increase by a factor of two for both the integrator and our predictors. In our simulations, we do not observe an increase in performance using an exponential forgetting factor adaptive LMMSE.