Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method

In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with...
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Autor*in:	Xin Cai [verfasserIn] Xiaozhou Zhu [verfasserIn] Wen Yao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Wireless Communications and Mobile Computing - Hindawi-Wiley, 2017, (2022)
Übergeordnetes Werk:	year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1155/2022/3262228

Katalog-ID:	DOAJ021126747

Internformat


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520			\|a In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller.
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publishDate	2022
allfields	10.1155/2022/3262228 doi (DE-627)DOAJ021126747 (DE-599)DOAJ15188a9fceba4efa9fb515ad86231c5b DE-627 ger DE-627 rakwb eng TK5101-6720 Xin Cai verfasserin aut Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller. Technology T Telecommunication Xiaozhou Zhu verfasserin aut Wen Yao verfasserin aut In Wireless Communications and Mobile Computing Hindawi-Wiley, 2017 (2022) (DE-627)328188395 (DE-600)2045240-8 15308677 nnns year:2022 https://doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/article/15188a9fceba4efa9fb515ad86231c5b kostenfrei http://dx.doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/toc/1530-8677 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
spelling	10.1155/2022/3262228 doi (DE-627)DOAJ021126747 (DE-599)DOAJ15188a9fceba4efa9fb515ad86231c5b DE-627 ger DE-627 rakwb eng TK5101-6720 Xin Cai verfasserin aut Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller. Technology T Telecommunication Xiaozhou Zhu verfasserin aut Wen Yao verfasserin aut In Wireless Communications and Mobile Computing Hindawi-Wiley, 2017 (2022) (DE-627)328188395 (DE-600)2045240-8 15308677 nnns year:2022 https://doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/article/15188a9fceba4efa9fb515ad86231c5b kostenfrei http://dx.doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/toc/1530-8677 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
allfields_unstemmed	10.1155/2022/3262228 doi (DE-627)DOAJ021126747 (DE-599)DOAJ15188a9fceba4efa9fb515ad86231c5b DE-627 ger DE-627 rakwb eng TK5101-6720 Xin Cai verfasserin aut Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller. Technology T Telecommunication Xiaozhou Zhu verfasserin aut Wen Yao verfasserin aut In Wireless Communications and Mobile Computing Hindawi-Wiley, 2017 (2022) (DE-627)328188395 (DE-600)2045240-8 15308677 nnns year:2022 https://doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/article/15188a9fceba4efa9fb515ad86231c5b kostenfrei http://dx.doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/toc/1530-8677 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
allfieldsGer	10.1155/2022/3262228 doi (DE-627)DOAJ021126747 (DE-599)DOAJ15188a9fceba4efa9fb515ad86231c5b DE-627 ger DE-627 rakwb eng TK5101-6720 Xin Cai verfasserin aut Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller. Technology T Telecommunication Xiaozhou Zhu verfasserin aut Wen Yao verfasserin aut In Wireless Communications and Mobile Computing Hindawi-Wiley, 2017 (2022) (DE-627)328188395 (DE-600)2045240-8 15308677 nnns year:2022 https://doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/article/15188a9fceba4efa9fb515ad86231c5b kostenfrei http://dx.doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/toc/1530-8677 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
allfieldsSound	10.1155/2022/3262228 doi (DE-627)DOAJ021126747 (DE-599)DOAJ15188a9fceba4efa9fb515ad86231c5b DE-627 ger DE-627 rakwb eng TK5101-6720 Xin Cai verfasserin aut Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller. Technology T Telecommunication Xiaozhou Zhu verfasserin aut Wen Yao verfasserin aut In Wireless Communications and Mobile Computing Hindawi-Wiley, 2017 (2022) (DE-627)328188395 (DE-600)2045240-8 15308677 nnns year:2022 https://doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/article/15188a9fceba4efa9fb515ad86231c5b kostenfrei http://dx.doi.org/10.1155/2022/3262228 kostenfrei https://doaj.org/toc/1530-8677 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
language	English
source	In Wireless Communications and Mobile Computing (2022) year:2022
sourceStr	In Wireless Communications and Mobile Computing (2022) year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Technology T Telecommunication
isfreeaccess_bool	true
container_title	Wireless Communications and Mobile Computing
authorswithroles_txt_mv	Xin Cai @@aut@@ Xiaozhou Zhu @@aut@@ Wen Yao @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	328188395
id	DOAJ021126747
language_de	englisch
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callnumber-first	T - Technology
author	Xin Cai
spellingShingle	Xin Cai misc TK5101-6720 misc Technology misc T misc Telecommunication Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method
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topic_title	TK5101-6720 Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method
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title	Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method
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title_full	Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method
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title_sort	design of the uav trajectory tracking system based on the adaptive neural network - adrc method
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title_auth	Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method
abstract	In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller.
abstractGer	In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller.
abstract_unstemmed	In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller.
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title_short	Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method
url	https://doi.org/10.1155/2022/3262228 https://doaj.org/article/15188a9fceba4efa9fb515ad86231c5b http://dx.doi.org/10.1155/2022/3262228 https://doaj.org/toc/1530-8677
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Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method

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