Effective control allocation using hierarchical multi-objective optimization for multi-phase flight
For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
SUN, Liguo [verfasserIn] ZHOU, Qing [verfasserIn] JIA, Baoxu [verfasserIn] TAN, Wenqian [verfasserIn] LI, Hangxu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Chinese journal of aeronautics - Amsterdam [u.a.] : Elsevier, 2002, 33, Seite 2002-2013 |
|---|---|
| Übergeordnetes Werk: |
volume:33 ; pages:2002-2013 |
| DOI / URN: |
10.1016/j.cja.2020.02.020 |
|---|
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ELV004373391 |
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| 520 | |a For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases. | ||
| 650 | 4 | |a Adaptive control | |
| 650 | 4 | |a Control allocation | |
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| 650 | 4 | |a Multi-phase and multi-objective | |
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| 700 | 1 | |a ZHOU, Qing |e verfasserin |4 aut | |
| 700 | 1 | |a JIA, Baoxu |e verfasserin |4 aut | |
| 700 | 1 | |a TAN, Wenqian |e verfasserin |4 aut | |
| 700 | 1 | |a LI, Hangxu |e verfasserin |4 aut | |
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10.1016/j.cja.2020.02.020 doi (DE-627)ELV004373391 (ELSEVIER)S1000-9361(20)30085-6 DE-627 ger DE-627 rda eng 380 DE-600 6,25 ssgn ASIEN DE-1a fid SUN, Liguo verfasserin aut Effective control allocation using hierarchical multi-objective optimization for multi-phase flight 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases. Adaptive control Control allocation Flying-wing aircraft Multi-phase and multi-objective Real-time optimization ZHOU, Qing verfasserin aut JIA, Baoxu verfasserin aut TAN, Wenqian verfasserin aut LI, Hangxu verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 33, Seite 2002-2013 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:33 pages:2002-2013 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 33 2002-2013 |
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10.1016/j.cja.2020.02.020 doi (DE-627)ELV004373391 (ELSEVIER)S1000-9361(20)30085-6 DE-627 ger DE-627 rda eng 380 DE-600 6,25 ssgn ASIEN DE-1a fid SUN, Liguo verfasserin aut Effective control allocation using hierarchical multi-objective optimization for multi-phase flight 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases. Adaptive control Control allocation Flying-wing aircraft Multi-phase and multi-objective Real-time optimization ZHOU, Qing verfasserin aut JIA, Baoxu verfasserin aut TAN, Wenqian verfasserin aut LI, Hangxu verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 33, Seite 2002-2013 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:33 pages:2002-2013 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 33 2002-2013 |
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10.1016/j.cja.2020.02.020 doi (DE-627)ELV004373391 (ELSEVIER)S1000-9361(20)30085-6 DE-627 ger DE-627 rda eng 380 DE-600 6,25 ssgn ASIEN DE-1a fid SUN, Liguo verfasserin aut Effective control allocation using hierarchical multi-objective optimization for multi-phase flight 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases. Adaptive control Control allocation Flying-wing aircraft Multi-phase and multi-objective Real-time optimization ZHOU, Qing verfasserin aut JIA, Baoxu verfasserin aut TAN, Wenqian verfasserin aut LI, Hangxu verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 33, Seite 2002-2013 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:33 pages:2002-2013 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 33 2002-2013 |
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10.1016/j.cja.2020.02.020 doi (DE-627)ELV004373391 (ELSEVIER)S1000-9361(20)30085-6 DE-627 ger DE-627 rda eng 380 DE-600 6,25 ssgn ASIEN DE-1a fid SUN, Liguo verfasserin aut Effective control allocation using hierarchical multi-objective optimization for multi-phase flight 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases. Adaptive control Control allocation Flying-wing aircraft Multi-phase and multi-objective Real-time optimization ZHOU, Qing verfasserin aut JIA, Baoxu verfasserin aut TAN, Wenqian verfasserin aut LI, Hangxu verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 33, Seite 2002-2013 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:33 pages:2002-2013 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 33 2002-2013 |
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10.1016/j.cja.2020.02.020 doi (DE-627)ELV004373391 (ELSEVIER)S1000-9361(20)30085-6 DE-627 ger DE-627 rda eng 380 DE-600 6,25 ssgn ASIEN DE-1a fid SUN, Liguo verfasserin aut Effective control allocation using hierarchical multi-objective optimization for multi-phase flight 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases. Adaptive control Control allocation Flying-wing aircraft Multi-phase and multi-objective Real-time optimization ZHOU, Qing verfasserin aut JIA, Baoxu verfasserin aut TAN, Wenqian verfasserin aut LI, Hangxu verfasserin aut Enthalten in Chinese journal of aeronautics Amsterdam [u.a.] : Elsevier, 2002 33, Seite 2002-2013 Online-Ressource (DE-627)534059384 (DE-600)2365081-3 (DE-576)267763506 1000-9361 nnns volume:33 pages:2002-2013 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2014 GBV_ILN_2068 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 33 2002-2013 |
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SUN, Liguo |
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SUN, Liguo ddc 380 ssgn 6,25 fid ASIEN misc Adaptive control misc Control allocation misc Flying-wing aircraft misc Multi-phase and multi-objective misc Real-time optimization Effective control allocation using hierarchical multi-objective optimization for multi-phase flight |
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380 DE-600 6,25 ssgn ASIEN DE-1a fid Effective control allocation using hierarchical multi-objective optimization for multi-phase flight Adaptive control Control allocation Flying-wing aircraft Multi-phase and multi-objective Real-time optimization |
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effective control allocation using hierarchical multi-objective optimization for multi-phase flight |
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Effective control allocation using hierarchical multi-objective optimization for multi-phase flight |
| abstract |
For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases. |
| abstractGer |
For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases. |
| abstract_unstemmed |
For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases. |
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Effective control allocation using hierarchical multi-objective optimization for multi-phase flight |
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