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Title: Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles
Authors: Sarabakha, Andriy
Imanberdiyev, Nursultan
Kayacan, Erdal
Khanesar, Mojtaba Ahmadieh
Hagras, Hani
Keywords: Sliding Mode Control
Fuzzy Neural Networks
Issue Date: 2017
Source: Sarabakha, A., Imanberdiyev, N., Kayacan, E., Khanesar, M. A., & Hagras, H. (2017). Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles. Information Sciences, 417, 361-380.
Series/Report no.: Information Sciences
Abstract: 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.
ISSN: 0020-0255
Rights: © 2017 Elsevier Inc. This is the author created version of a work that has been peer reviewed and accepted for publication by Information Sciences, Elsevier Inc. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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