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

Erdal Kayacan*, Mojtaba Ahmadieh Khanesar, Jaime Rubio-Hervas, Mahmut Reyhanoglu

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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.

Original languageEnglish
Article number5402809
JournalInternational Journal of Aerospace Engineering
Volume2017
ISSN1687-5966
DOIs
Publication statusPublished - 2017
Externally publishedYes

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