Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method

In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, lo...
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Autor*in:	Luo Zhe [verfasserIn] Li Xinsan [verfasserIn] Wang Lixin [verfasserIn] Shen Qiang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: Mathematical Problems in Engineering - Hindawi Limited, 2002, (2021)
Übergeordnetes Werk:	year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1155/2021/6652232

Katalog-ID:	DOAJ004010329

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520			\|a In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively.
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allfields	10.1155/2021/6652232 doi (DE-627)DOAJ004010329 (DE-599)DOAJf89cbc4102e2433fa9898a9aa91136f8 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Luo Zhe verfasserin aut Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively. Engineering (General). Civil engineering (General) Mathematics Li Xinsan verfasserin aut Wang Lixin verfasserin aut Shen Qiang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/article/f89cbc4102e2433fa9898a9aa91136f8 kostenfrei http://dx.doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 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_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
spelling	10.1155/2021/6652232 doi (DE-627)DOAJ004010329 (DE-599)DOAJf89cbc4102e2433fa9898a9aa91136f8 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Luo Zhe verfasserin aut Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively. Engineering (General). Civil engineering (General) Mathematics Li Xinsan verfasserin aut Wang Lixin verfasserin aut Shen Qiang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/article/f89cbc4102e2433fa9898a9aa91136f8 kostenfrei http://dx.doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 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_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
allfields_unstemmed	10.1155/2021/6652232 doi (DE-627)DOAJ004010329 (DE-599)DOAJf89cbc4102e2433fa9898a9aa91136f8 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Luo Zhe verfasserin aut Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively. Engineering (General). Civil engineering (General) Mathematics Li Xinsan verfasserin aut Wang Lixin verfasserin aut Shen Qiang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/article/f89cbc4102e2433fa9898a9aa91136f8 kostenfrei http://dx.doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 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_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
allfieldsGer	10.1155/2021/6652232 doi (DE-627)DOAJ004010329 (DE-599)DOAJf89cbc4102e2433fa9898a9aa91136f8 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Luo Zhe verfasserin aut Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively. Engineering (General). Civil engineering (General) Mathematics Li Xinsan verfasserin aut Wang Lixin verfasserin aut Shen Qiang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/article/f89cbc4102e2433fa9898a9aa91136f8 kostenfrei http://dx.doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 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_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
allfieldsSound	10.1155/2021/6652232 doi (DE-627)DOAJ004010329 (DE-599)DOAJf89cbc4102e2433fa9898a9aa91136f8 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Luo Zhe verfasserin aut Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively. Engineering (General). Civil engineering (General) Mathematics Li Xinsan verfasserin aut Wang Lixin verfasserin aut Shen Qiang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/article/f89cbc4102e2433fa9898a9aa91136f8 kostenfrei http://dx.doi.org/10.1155/2021/6652232 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 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_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
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callnumber-first	T - Technology
author	Luo Zhe
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title	Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method
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title_full	Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method
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title_sort	multiconstrained gliding guidance based on optimal and reinforcement learning method
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title_auth	Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method
abstract	In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively.
abstractGer	In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively.
abstract_unstemmed	In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively.
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title_short	Multiconstrained Gliding Guidance Based on Optimal and Reinforcement Learning Method
url	https://doi.org/10.1155/2021/6652232 https://doaj.org/article/f89cbc4102e2433fa9898a9aa91136f8 http://dx.doi.org/10.1155/2021/6652232 https://doaj.org/toc/1024-123X https://doaj.org/toc/1563-5147
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doi_str	10.1155/2021/6652232
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up_date	2024-07-03T21:27:07.430Z
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