Nonlinear adaptive tracking control for a small-scale unmanned helicopter using a learning algorithm with the least parameters
This paper puts forward a new nonlinear adaptive controller for a small-scale unmanned helicopter with unknown mass. The controller is developed under the framework of backstepping technique, with the unknown mass estimated by a novel identifier and the internal and external uncertainties approximat...
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| Published in | Nonlinear dynamics Vol. 89; no. 2; pp. 1289 - 1308 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.07.2017
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0924-090X 1573-269X |
| DOI | 10.1007/s11071-017-3516-z |
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| Abstract | This paper puts forward a new nonlinear adaptive controller for a small-scale unmanned helicopter with unknown mass. The controller is developed under the framework of backstepping technique, with the unknown mass estimated by a novel identifier and the internal and external uncertainties approximated by radial basis function neural networks (RBFNNs). The overall closed-loop system, which consists of three parts: longitudinal–lateral subsystem, heave subsystem, and heading subsystem, is proved to be semi-globally uniformly ultimately bounded by the strict Lyapunov stability theory. Furthermore, the proposed method is more practical in actual applications with an improved online learning algorithm of the least parameters used in the RBFNNs. Finally, the effectiveness and the robustness of the proposed strategy are exhibited through two simulations compared with the classic PID method. |
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| AbstractList | This paper puts forward a new nonlinear adaptive controller for a small-scale unmanned helicopter with unknown mass. The controller is developed under the framework of backstepping technique, with the unknown mass estimated by a novel identifier and the internal and external uncertainties approximated by radial basis function neural networks (RBFNNs). The overall closed-loop system, which consists of three parts: longitudinal–lateral subsystem, heave subsystem, and heading subsystem, is proved to be semi-globally uniformly ultimately bounded by the strict Lyapunov stability theory. Furthermore, the proposed method is more practical in actual applications with an improved online learning algorithm of the least parameters used in the RBFNNs. Finally, the effectiveness and the robustness of the proposed strategy are exhibited through two simulations compared with the classic PID method. |
| Author | Zhou, Bin Tang, Shuai Li, Zhibin Zheng, Zhiqiang |
| Author_xml | – sequence: 1 givenname: Bin orcidid: 0000-0002-7242-6647 surname: Zhou fullname: Zhou, Bin email: zhoubin@nudt.edu.cn organization: College of Mechatronic Engineering and Automation, National University of Defense Technology – sequence: 2 givenname: Zhibin surname: Li fullname: Li, Zhibin organization: College of Mechatronic Engineering and Automation, National University of Defense Technology – sequence: 3 givenname: Zhiqiang surname: Zheng fullname: Zheng, Zhiqiang organization: College of Mechatronic Engineering and Automation, National University of Defense Technology – sequence: 4 givenname: Shuai surname: Tang fullname: Tang, Shuai organization: Department of Machinery and Electrical Engineering, Logistical Engineering University |
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| CitedBy_id | crossref_primary_10_1007_s11071_019_04923_9 crossref_primary_10_1016_j_ymssp_2020_107022 crossref_primary_10_1007_s11071_021_07067_x crossref_primary_10_1109_TSMC_2023_3298034 crossref_primary_10_1007_s10957_021_01900_4 crossref_primary_10_1007_s11071_019_05301_1 crossref_primary_10_1007_s11071_024_09689_3 crossref_primary_10_1109_ACCESS_2019_2946855 crossref_primary_10_1007_s11071_022_07433_3 |
| Cites_doi | 10.1109/TCST.2015.2396851 10.15388/Informatica.2014.20 10.1109/TCST.2010.2042450 10.1109/TAC.2009.2015562 10.1007/s11071-013-1031-4 10.1007/s11071-016-2788-z 10.2514/1.10336 10.1109/TNNLS.2015.2406812 10.1016/S0005-1098(03)00168-7 10.1007/s11071-015-2473-7 10.1007/s12555-014-0244-8 10.1016/j.mechatronics.2010.04.009 10.1007/978-3-642-34816-7 10.1109/TNNLS.2014.2360724 10.2514/1.31377 10.1109/TIE.2012.2216233 10.1109/TNNLS.2013.2251747 10.1109/TRO.2014.2363371 10.1007/978-1-4757-3785-1 10.1007/s11071-014-1425-y 10.1109/TCST.2010.2102023 10.1007/s11071-014-1552-5 10.2514/1.8980 10.1016/j.automatica.2009.06.016 10.1109/TNN.2009.2034145 10.1007/s10846-012-9720-7 10.1016/j.conengprac.2013.12.004 10.1016/j.automatica.2007.03.028 10.1016/j.mechatronics.2011.02.002 10.1007/s11071-014-1843-x 10.1007/s11071-015-2387-4 10.1016/j.conengprac.2013.03.009 10.2514/6.2008-7414 10.1109/TASE.2016.2517155 10.1109/ChiCC.2015.7260076 10.2514/6.2008-6780 10.1109/ACC.2009.5159846 10.1109/ICInfA.2015.7279704 10.1109/IJCNN.2007.4370992 |
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| Snippet | This paper puts forward a new nonlinear adaptive controller for a small-scale unmanned helicopter with unknown mass. The controller is developed under the... |
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| SubjectTerms | Adaptive algorithms Adaptive control Algorithms Approximation Automotive Engineering Classical Mechanics Computer simulation Control Controllers Distance learning Dynamical Systems Engineering Feedback control Heaving Helicopter control Machine learning Mechanical Engineering Neural networks Nonlinear control Original Paper Parameters Radial basis function Robustness Subsystems Tracking control Unmanned helicopters Vibration |
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| Title | Nonlinear adaptive tracking control for a small-scale unmanned helicopter using a learning algorithm with the least parameters |
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