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Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

Sarabakha, A and Imanberdiyev, N and Kayacan, E and Khanesar, MA and Hagras, H (2017) 'Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles.' Information Sciences, 417. pp. 361-380. ISSN 0020-0255

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In this paper, Levenberg–Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which fuzzy neural network is able to learn the quadrotor's control model on-line. The proposed design allows handling uncertainties and lack of modelling at a computationally inexpensive cost. The parameter update rules of the learning algorithms are derived based on a Levenberg–Marquardt inspired approach, and the proof of the stability of two proposed control laws are verified by using the Lyapunov stability theory. In order to evaluate the performance of the proposed controllers extensive simulations and real-time experiments are conducted. The 3D trajectory tracking problem for a quadrotor is considered in the presence of time-varying wind conditions.

Item Type: Article
Additional Information: publisher: Elsevier articletitle: Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles journaltitle: Information Sciences articlelink: content_type: article copyright: © 2017 Elsevier Inc. All rights reserved.
Uncontrolled Keywords: Fuzzy neural networks; Sliding mode control; Levenberg-Marquardt algorithm; Type-1 fuzzy logic control; Unmanned aerial vehicle
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 14 Jul 2017 13:31
Last Modified: 15 Jan 2022 01:02

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