Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85156
Title: Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks
Authors: Kayacan, Erdal
Khanesar, Mojtaba Ahmadieh
Rubio-Hervas, Jaime
Reyhanoglu, Mahmut
Keywords: Unmanned aerial vehicles
Fuzzy neural networks
Issue Date: 2017
Source: Kayacan, E., Khanesar, M. A., Rubio-Hervas, J., & Reyhanoglu, M. (2017). Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks. International Journal of Aerospace Engineering, 2017, 5402809-.
Series/Report no.: International Journal of Aerospace Engineering
Abstract: 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.
URI: https://hdl.handle.net/10356/85156
http://hdl.handle.net/10220/43648
ISSN: 1687-5966
DOI: 10.1155/2017/5402809
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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