Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles
Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid exte...
Ausführliche Beschreibung
Autor*in: |
Homayouni Amlashi, Abbas [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Schlagwörter: |
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Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2017 |
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Übergeordnetes Werk: |
Enthalten in: International journal of dynamics and control - Berlin : Springer, 2013, 6(2017), 2 vom: 30. Mai, Seite 707-725 |
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Übergeordnetes Werk: |
volume:6 ; year:2017 ; number:2 ; day:30 ; month:05 ; pages:707-725 |
Links: |
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DOI / URN: |
10.1007/s40435-017-0333-7 |
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Katalog-ID: |
SPR036473472 |
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520 | |a Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties. | ||
650 | 4 | |a Flight control |7 (dpeaa)DE-He213 | |
650 | 4 | |a Nonlinear dynamic inversion |7 (dpeaa)DE-He213 | |
650 | 4 | |a Hybrid extended Kalman filter |7 (dpeaa)DE-He213 | |
650 | 4 | |a Nonlinear regulator |7 (dpeaa)DE-He213 | |
650 | 4 | |a Trajectory tracking |7 (dpeaa)DE-He213 | |
700 | 1 | |a Mojed Gharamaleki, Reza |4 aut | |
700 | 1 | |a Hamidi Nejad, Mohammad Hossein |4 aut | |
700 | 1 | |a Mirzaei, Mehdi |4 aut | |
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10.1007/s40435-017-0333-7 doi (DE-627)SPR036473472 (SPR)s40435-017-0333-7-e DE-627 ger DE-627 rakwb eng Homayouni Amlashi, Abbas verfasserin aut Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2017 Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties. Flight control (dpeaa)DE-He213 Nonlinear dynamic inversion (dpeaa)DE-He213 Hybrid extended Kalman filter (dpeaa)DE-He213 Nonlinear regulator (dpeaa)DE-He213 Trajectory tracking (dpeaa)DE-He213 Mojed Gharamaleki, Reza aut Hamidi Nejad, Mohammad Hossein aut Mirzaei, Mehdi aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 6(2017), 2 vom: 30. Mai, Seite 707-725 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:6 year:2017 number:2 day:30 month:05 pages:707-725 https://dx.doi.org/10.1007/s40435-017-0333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 6 2017 2 30 05 707-725 |
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10.1007/s40435-017-0333-7 doi (DE-627)SPR036473472 (SPR)s40435-017-0333-7-e DE-627 ger DE-627 rakwb eng Homayouni Amlashi, Abbas verfasserin aut Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2017 Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties. Flight control (dpeaa)DE-He213 Nonlinear dynamic inversion (dpeaa)DE-He213 Hybrid extended Kalman filter (dpeaa)DE-He213 Nonlinear regulator (dpeaa)DE-He213 Trajectory tracking (dpeaa)DE-He213 Mojed Gharamaleki, Reza aut Hamidi Nejad, Mohammad Hossein aut Mirzaei, Mehdi aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 6(2017), 2 vom: 30. Mai, Seite 707-725 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:6 year:2017 number:2 day:30 month:05 pages:707-725 https://dx.doi.org/10.1007/s40435-017-0333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 6 2017 2 30 05 707-725 |
allfields_unstemmed |
10.1007/s40435-017-0333-7 doi (DE-627)SPR036473472 (SPR)s40435-017-0333-7-e DE-627 ger DE-627 rakwb eng Homayouni Amlashi, Abbas verfasserin aut Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2017 Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties. Flight control (dpeaa)DE-He213 Nonlinear dynamic inversion (dpeaa)DE-He213 Hybrid extended Kalman filter (dpeaa)DE-He213 Nonlinear regulator (dpeaa)DE-He213 Trajectory tracking (dpeaa)DE-He213 Mojed Gharamaleki, Reza aut Hamidi Nejad, Mohammad Hossein aut Mirzaei, Mehdi aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 6(2017), 2 vom: 30. Mai, Seite 707-725 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:6 year:2017 number:2 day:30 month:05 pages:707-725 https://dx.doi.org/10.1007/s40435-017-0333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 6 2017 2 30 05 707-725 |
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10.1007/s40435-017-0333-7 doi (DE-627)SPR036473472 (SPR)s40435-017-0333-7-e DE-627 ger DE-627 rakwb eng Homayouni Amlashi, Abbas verfasserin aut Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2017 Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties. Flight control (dpeaa)DE-He213 Nonlinear dynamic inversion (dpeaa)DE-He213 Hybrid extended Kalman filter (dpeaa)DE-He213 Nonlinear regulator (dpeaa)DE-He213 Trajectory tracking (dpeaa)DE-He213 Mojed Gharamaleki, Reza aut Hamidi Nejad, Mohammad Hossein aut Mirzaei, Mehdi aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 6(2017), 2 vom: 30. Mai, Seite 707-725 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:6 year:2017 number:2 day:30 month:05 pages:707-725 https://dx.doi.org/10.1007/s40435-017-0333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 6 2017 2 30 05 707-725 |
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10.1007/s40435-017-0333-7 doi (DE-627)SPR036473472 (SPR)s40435-017-0333-7-e DE-627 ger DE-627 rakwb eng Homayouni Amlashi, Abbas verfasserin aut Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2017 Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties. Flight control (dpeaa)DE-He213 Nonlinear dynamic inversion (dpeaa)DE-He213 Hybrid extended Kalman filter (dpeaa)DE-He213 Nonlinear regulator (dpeaa)DE-He213 Trajectory tracking (dpeaa)DE-He213 Mojed Gharamaleki, Reza aut Hamidi Nejad, Mohammad Hossein aut Mirzaei, Mehdi aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 6(2017), 2 vom: 30. Mai, Seite 707-725 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:6 year:2017 number:2 day:30 month:05 pages:707-725 https://dx.doi.org/10.1007/s40435-017-0333-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4393 GBV_ILN_4700 AR 6 2017 2 30 05 707-725 |
language |
English |
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Enthalten in International journal of dynamics and control 6(2017), 2 vom: 30. Mai, Seite 707-725 volume:6 year:2017 number:2 day:30 month:05 pages:707-725 |
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Enthalten in International journal of dynamics and control 6(2017), 2 vom: 30. Mai, Seite 707-725 volume:6 year:2017 number:2 day:30 month:05 pages:707-725 |
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Flight control Nonlinear dynamic inversion Hybrid extended Kalman filter Nonlinear regulator Trajectory tracking |
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International journal of dynamics and control |
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Homayouni Amlashi, Abbas @@aut@@ Mojed Gharamaleki, Reza @@aut@@ Hamidi Nejad, Mohammad Hossein @@aut@@ Mirzaei, Mehdi @@aut@@ |
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2017-05-30T00:00:00Z |
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Homayouni Amlashi, Abbas |
spellingShingle |
Homayouni Amlashi, Abbas misc Flight control misc Nonlinear dynamic inversion misc Hybrid extended Kalman filter misc Nonlinear regulator misc Trajectory tracking Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles |
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Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles Flight control (dpeaa)DE-He213 Nonlinear dynamic inversion (dpeaa)DE-He213 Hybrid extended Kalman filter (dpeaa)DE-He213 Nonlinear regulator (dpeaa)DE-He213 Trajectory tracking (dpeaa)DE-He213 |
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misc Flight control misc Nonlinear dynamic inversion misc Hybrid extended Kalman filter misc Nonlinear regulator misc Trajectory tracking |
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misc Flight control misc Nonlinear dynamic inversion misc Hybrid extended Kalman filter misc Nonlinear regulator misc Trajectory tracking |
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misc Flight control misc Nonlinear dynamic inversion misc Hybrid extended Kalman filter misc Nonlinear regulator misc Trajectory tracking |
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Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles |
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Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles |
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Homayouni Amlashi, Abbas |
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International journal of dynamics and control |
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Homayouni Amlashi, Abbas Mojed Gharamaleki, Reza Hamidi Nejad, Mohammad Hossein Mirzaei, Mehdi |
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design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles |
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Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles |
abstract |
Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties. © Springer-Verlag Berlin Heidelberg 2017 |
abstractGer |
Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties. © Springer-Verlag Berlin Heidelberg 2017 |
abstract_unstemmed |
Abstract For the purpose of trajectory tracking with aerial vehicles, a hybrid extended Kalman filter and a nonlinear regulator are designed to increase the robustness of the nonlinear dynamic inversion controller against modellisng uncertainties and atmospheric disturbances. The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties. © Springer-Verlag Berlin Heidelberg 2017 |
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Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles |
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https://dx.doi.org/10.1007/s40435-017-0333-7 |
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Mojed Gharamaleki, Reza Hamidi Nejad, Mohammad Hossein Mirzaei, Mehdi |
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The goal of hybrid extended Kalman filter is the online estimation of aerodynamic forces and moments acting on the vehicle using discrete measurements of the noisy sensors. Moreover, hybrid extended Kalman filter considers any uncertainties or un-modeled dynamics as additional external forces and moments. This specification of hybrid extended Kalman filter helps the nonlinear dynamic inversion controller to successfully cancel the nonlinearities between the inputs and outputs in presence of uncertainties. To improve the robustness of the aerial vehicle against atmospheric disturbances, a nonlinear regulator is also designed to regulate the aircraft angular rate perturbations and assist the nonlinear dynamic inversion controller to maintain the desired attitude. The simulation results illustrate the effectiveness of the proposed estimator-based controllers in guiding the NASA HL-20 reentry vehicle to a desired challenging trajectory in the presence of atmospheric disturbances and vehicle parametric uncertainties.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Flight control</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonlinear dynamic inversion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hybrid extended Kalman filter</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonlinear regulator</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Trajectory tracking</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mojed Gharamaleki, Reza</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hamidi Nejad, Mohammad Hossein</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mirzaei, Mehdi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of dynamics and control</subfield><subfield code="d">Berlin : Springer, 2013</subfield><subfield code="g">6(2017), 2 vom: 30. Mai, Seite 707-725</subfield><subfield code="w">(DE-627)745617794</subfield><subfield code="w">(DE-600)2714518-9</subfield><subfield code="x">2195-2698</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:2</subfield><subfield code="g">day:30</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:707-725</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s40435-017-0333-7</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" 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