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Automatic Landing Control System Design Using Adaptive Neural Network and Its Hardware Realization

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3 Author(s)
Jih-Gau Juang ; National Taiwan Ocean University, Keelung, Taiwan, ROC ; Li-Hsiang Chien ; Felix Lin

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.

Published in:

IEEE Systems Journal  (Volume:5 ,  Issue: 2 )