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Computational Intelligence and Neuroscience
Volume 2017, Article ID 8575703, 10 pages
https://doi.org/10.1155/2017/8575703
Research Article

Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

1Key Laboratory of Unmanned Aerial Vehicle Technology of Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Correspondence should be addressed to YanBin Liu; moc.931@nibnayuil_aaun

Received 4 January 2017; Accepted 29 November 2017; Published 19 December 2017

Academic Editor: Silvia Conforto

Copyright © 2017 YanBin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller.