Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks
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 co...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/5402809 |
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| _version_ | 1850231761223548928 |
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| author | Erdal Kayacan Mojtaba Ahmadieh Khanesar Jaime Rubio-Hervas Mahmut Reyhanoglu |
| author_facet | Erdal Kayacan Mojtaba Ahmadieh Khanesar Jaime Rubio-Hervas Mahmut Reyhanoglu |
| author_sort | Erdal Kayacan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-03444dd8cc9d4c9b83c4c3e6ab232233 |
| institution | OA Journals |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-03444dd8cc9d4c9b83c4c3e6ab2322332025-08-20T02:03:25ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742017-01-01201710.1155/2017/54028095402809Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural NetworksErdal Kayacan0Mojtaba Ahmadieh Khanesar1Jaime Rubio-Hervas2Mahmut Reyhanoglu3School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, SingaporeFaculty of Electrical and Computer Engineering, Semnan University, Semnan 35131, IranInfinium Robotics Pte Ltd., 128381, SingaporePhysical Sciences Department, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USAA 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.http://dx.doi.org/10.1155/2017/5402809 |
| spellingShingle | Erdal Kayacan Mojtaba Ahmadieh Khanesar Jaime Rubio-Hervas Mahmut Reyhanoglu Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks International Journal of Aerospace Engineering |
| title | Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks |
| title_full | Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks |
| title_fullStr | Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks |
| title_full_unstemmed | Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks |
| title_short | Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks |
| title_sort | learning control of fixed wing unmanned aerial vehicles using fuzzy neural networks |
| url | http://dx.doi.org/10.1155/2017/5402809 |
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