Abstract
The study of neural network controller has become increasingly mature as plenty of simulations were conducted to verify the theoretical analysis. In this paper, a neural network model based flight control system is proposed. This model is capable of sensing interference caused by change of the mathematical model of aircraft, so that it can start or stop the learning process automatically. The flight controller equipped with this model can adjust the learning step due to the intensity of interference during the learning process. Furthermore, the convergence time of control error is shortened to a ultra low level. Comparative experiments on aircraft confirm that the proposed neural network model has high adaptability, which illustrates its good performance under different aircraft models.
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Liang, J., Du, W., Xing, K., Zhong, C. (2017). A Neural Network Model Based Adaptive Flight Control System. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_69
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DOI: https://doi.org/10.1007/978-3-319-60033-8_69
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