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Renewable energy technologies have recently attracted intensive attentions. Several renewable energies, such as wind, solar, etc., are being investigated to solve current energy crisis. In this paper, a nonlinear controller is developed for tracking control of a wind energy conversion system under a hierarchical configuration. First, an adaptive neural-network-based estimator is developed to estimate uncertain (possibly unknown) aerodynamics online. Based on an estimated value of aerodynamics, a model-based adaptive tracking control law is derived based on the Lyapunov stability analysis. Associated dynamic parameter estimators are also developed to remove requirement of prior knowledge of system parameters. Then, robust differentiator techniques are utilized to eliminate the need for an acceleration of the wind and rotor. It is shown that the proposed controller can regulate tracking error to an arbitrary small value even if neither system parameter nor aerodynamics is available for control design. It is expected that the proposed control algorithm can be used as an “universal controller” for similar types of variable speed wind turbine with minimal modifications. The modularity of the proposed controller will enjoy the plug-and-play property that will be helpful in distributed control of smart grids. Simulation studies are performed along with the theoretical analysis to validate the proposed method.