Research Repository

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. 361 - 380. ISSN 0020-0255

1-s2.0-S0020025517308393-main.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB) | Preview


© 2017 Elsevier Inc. 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
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 14 Jul 2017 13:31
Last Modified: 04 Feb 2019 11:17

Actions (login required)

View Item View Item