Adaptive control of aircraft dynamics using neural networks

In this paper a neural network model-based predictive control strategy for aircraft systems with unknown parameters is presented. The objective of the paper is to stabilize unknown systems by the adaptive control law through an optimization procedure in which a cost function representing the deviation error between set-points and the predicted outputs obtained from a neural network is minimized. Due to its capability of characterizing dynamic functional relationships and its feedback processing structure, a recurrent neural network is employed as an adaptive estimator for future state values. The neural network training is performed by the dynamic sequential recursive backpropagation learning algorithm, which allows the neural network to be trained on-line. It is shown that the proposed neural network learning algorithm has potential for designing flight control systems which can compensate for unpredictable changes in an aircraft dynamics over a wide range of flight conditions and other uncertainties.