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International Journal of Aerospace Engineering
Volume 2017, Article ID 5402809, 12 pages
Research Article

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

1School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798
2Faculty of Electrical and Computer Engineering, Semnan University, Semnan 35131, Iran
3Infinium Robotics Pte Ltd., Singapore 128381
4Physical Sciences Department, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA

Correspondence should be addressed to Erdal Kayacan; gs.ude.utn@ladre

Received 21 August 2016; Revised 25 December 2016; Accepted 26 December 2016; Published 9 February 2017

Academic Editor: Christopher J. Damaren

Copyright © 2017 Erdal Kayacan 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.


A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN) is used in parallel with a conventional P (proportional) controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC) theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs). The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to time-varying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.