Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles
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...
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| Published in | Information sciences Vol. 417; pp. 361 - 380 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.11.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-0255 1872-6291 1872-6291 |
| DOI | 10.1016/j.ins.2017.07.020 |
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| 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. |
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| AbstractList | 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. |
| Author | Sarabakha, Andriy Hagras, Hani Khanesar, Mojtaba Ahmadieh Imanberdiyev, Nursultan Kayacan, Erdal |
| Author_xml | – sequence: 1 givenname: Andriy surname: Sarabakha fullname: Sarabakha, Andriy email: andriy001@e.ntu.edu.sg organization: School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore – sequence: 2 givenname: Nursultan surname: Imanberdiyev fullname: Imanberdiyev, Nursultan email: iman0005@e.ntu.edu.sg organization: School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore – sequence: 3 givenname: Erdal surname: Kayacan fullname: Kayacan, Erdal email: erdal@ntu.edu.sg, erdal.kayacan@gmail.com organization: School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore – sequence: 4 givenname: Mojtaba Ahmadieh surname: Khanesar fullname: Khanesar, Mojtaba Ahmadieh email: ahmadieh@semnan.ac.ir organization: Faculty of Electrical and Computer Engineering, Semnan University, Semnan, 35131, Iran – sequence: 5 givenname: Hani surname: Hagras fullname: Hagras, Hani email: hani@essex.ac.uk organization: The Computational Intelligence Centre, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom |
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| Keywords | Fuzzy neural networks Type-1 fuzzy logic control Levenberg–Marquardt algorithm Unmanned aerial vehicle Sliding mode control |
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| SubjectTerms | Fuzzy neural networks Levenberg–Marquardt algorithm Sliding mode control Type-1 fuzzy logic control Unmanned aerial vehicle |
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| Title | Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles |
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