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This paper presents an adaptive neural network, designed to improve the performance of conventional automatic landing systems (ALS). Real-time learning was applied to train the neural network using the gradient-descent of an error function to adaptively update weights. Adaptive learning rates were obtained through the analysis of Lyapunov stability to guarantee the convergence of learning. In addition, we applied a DSP controller using the VisSim/TI C2000 Rapid Prototyper to develop an embedded control system and establish on-line real-time control. Simulations show that the proposed control scheme has superior performance to conventional ALS under conditions of wind disturbance of up to 75 ft/s.