A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind
Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will...
Ausführliche Beschreibung
Autor*in: |
Nobahari, Hadi [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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Übergeordnetes Werk: |
Enthalten in: Journal of intelligent and robotic systems - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988, 105(2022), 1 vom: Mai |
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Übergeordnetes Werk: |
volume:105 ; year:2022 ; number:1 ; month:05 |
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DOI / URN: |
10.1007/s10846-022-01635-y |
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Katalog-ID: |
SPR046942173 |
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520 | |a Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function is defined to penalize the distance from the desired path and the control effort. Then, the parameters of the fuzzy system are optimized for different initial and wind conditions. During the flight, the optimized fuzzy systems determine the parameters of the guidance algorithm in an online manner, in such a way that the desired path is better followed. The obtained results show that online adjustment of the PLOS guidance law improves its performance in the presence of wind by about 20%. | ||
650 | 4 | |a Trajectory tracking |7 (dpeaa)DE-He213 | |
650 | 4 | |a PLOS guidance |7 (dpeaa)DE-He213 | |
650 | 4 | |a Pursuit guidance |7 (dpeaa)DE-He213 | |
650 | 4 | |a LOS guidance |7 (dpeaa)DE-He213 | |
650 | 4 | |a Fuzzy system |7 (dpeaa)DE-He213 | |
650 | 4 | |a Parameter optimization |7 (dpeaa)DE-He213 | |
700 | 1 | |a Asghari, Javad |4 aut | |
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10.1007/s10846-022-01635-y doi (DE-627)SPR046942173 (SPR)s10846-022-01635-y-e DE-627 ger DE-627 rakwb eng Nobahari, Hadi verfasserin (orcid)0000-0002-3579-1207 aut A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function is defined to penalize the distance from the desired path and the control effort. Then, the parameters of the fuzzy system are optimized for different initial and wind conditions. During the flight, the optimized fuzzy systems determine the parameters of the guidance algorithm in an online manner, in such a way that the desired path is better followed. The obtained results show that online adjustment of the PLOS guidance law improves its performance in the presence of wind by about 20%. Trajectory tracking (dpeaa)DE-He213 PLOS guidance (dpeaa)DE-He213 Pursuit guidance (dpeaa)DE-He213 LOS guidance (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Parameter optimization (dpeaa)DE-He213 Asghari, Javad aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 105(2022), 1 vom: Mai (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:105 year:2022 number:1 month:05 https://dx.doi.org/10.1007/s10846-022-01635-y 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2022 1 05 |
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10.1007/s10846-022-01635-y doi (DE-627)SPR046942173 (SPR)s10846-022-01635-y-e DE-627 ger DE-627 rakwb eng Nobahari, Hadi verfasserin (orcid)0000-0002-3579-1207 aut A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function is defined to penalize the distance from the desired path and the control effort. Then, the parameters of the fuzzy system are optimized for different initial and wind conditions. During the flight, the optimized fuzzy systems determine the parameters of the guidance algorithm in an online manner, in such a way that the desired path is better followed. The obtained results show that online adjustment of the PLOS guidance law improves its performance in the presence of wind by about 20%. Trajectory tracking (dpeaa)DE-He213 PLOS guidance (dpeaa)DE-He213 Pursuit guidance (dpeaa)DE-He213 LOS guidance (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Parameter optimization (dpeaa)DE-He213 Asghari, Javad aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 105(2022), 1 vom: Mai (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:105 year:2022 number:1 month:05 https://dx.doi.org/10.1007/s10846-022-01635-y 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2022 1 05 |
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10.1007/s10846-022-01635-y doi (DE-627)SPR046942173 (SPR)s10846-022-01635-y-e DE-627 ger DE-627 rakwb eng Nobahari, Hadi verfasserin (orcid)0000-0002-3579-1207 aut A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function is defined to penalize the distance from the desired path and the control effort. Then, the parameters of the fuzzy system are optimized for different initial and wind conditions. During the flight, the optimized fuzzy systems determine the parameters of the guidance algorithm in an online manner, in such a way that the desired path is better followed. The obtained results show that online adjustment of the PLOS guidance law improves its performance in the presence of wind by about 20%. Trajectory tracking (dpeaa)DE-He213 PLOS guidance (dpeaa)DE-He213 Pursuit guidance (dpeaa)DE-He213 LOS guidance (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Parameter optimization (dpeaa)DE-He213 Asghari, Javad aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 105(2022), 1 vom: Mai (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:105 year:2022 number:1 month:05 https://dx.doi.org/10.1007/s10846-022-01635-y 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2022 1 05 |
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10.1007/s10846-022-01635-y doi (DE-627)SPR046942173 (SPR)s10846-022-01635-y-e DE-627 ger DE-627 rakwb eng Nobahari, Hadi verfasserin (orcid)0000-0002-3579-1207 aut A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function is defined to penalize the distance from the desired path and the control effort. Then, the parameters of the fuzzy system are optimized for different initial and wind conditions. During the flight, the optimized fuzzy systems determine the parameters of the guidance algorithm in an online manner, in such a way that the desired path is better followed. The obtained results show that online adjustment of the PLOS guidance law improves its performance in the presence of wind by about 20%. Trajectory tracking (dpeaa)DE-He213 PLOS guidance (dpeaa)DE-He213 Pursuit guidance (dpeaa)DE-He213 LOS guidance (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Parameter optimization (dpeaa)DE-He213 Asghari, Javad aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 105(2022), 1 vom: Mai (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:105 year:2022 number:1 month:05 https://dx.doi.org/10.1007/s10846-022-01635-y 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2022 1 05 |
allfieldsSound |
10.1007/s10846-022-01635-y doi (DE-627)SPR046942173 (SPR)s10846-022-01635-y-e DE-627 ger DE-627 rakwb eng Nobahari, Hadi verfasserin (orcid)0000-0002-3579-1207 aut A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function is defined to penalize the distance from the desired path and the control effort. Then, the parameters of the fuzzy system are optimized for different initial and wind conditions. During the flight, the optimized fuzzy systems determine the parameters of the guidance algorithm in an online manner, in such a way that the desired path is better followed. The obtained results show that online adjustment of the PLOS guidance law improves its performance in the presence of wind by about 20%. Trajectory tracking (dpeaa)DE-He213 PLOS guidance (dpeaa)DE-He213 Pursuit guidance (dpeaa)DE-He213 LOS guidance (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Parameter optimization (dpeaa)DE-He213 Asghari, Javad aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 105(2022), 1 vom: Mai (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:105 year:2022 number:1 month:05 https://dx.doi.org/10.1007/s10846-022-01635-y 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 105 2022 1 05 |
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Nobahari, Hadi |
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Nobahari, Hadi misc Trajectory tracking misc PLOS guidance misc Pursuit guidance misc LOS guidance misc Fuzzy system misc Parameter optimization A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind |
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A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind Trajectory tracking (dpeaa)DE-He213 PLOS guidance (dpeaa)DE-He213 Pursuit guidance (dpeaa)DE-He213 LOS guidance (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Parameter optimization (dpeaa)DE-He213 |
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A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind |
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A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind |
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fuzzy-plos guidance law for precise trajectory tracking of a uav in the presence of wind |
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A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind |
abstract |
Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function is defined to penalize the distance from the desired path and the control effort. Then, the parameters of the fuzzy system are optimized for different initial and wind conditions. During the flight, the optimized fuzzy systems determine the parameters of the guidance algorithm in an online manner, in such a way that the desired path is better followed. The obtained results show that online adjustment of the PLOS guidance law improves its performance in the presence of wind by about 20%. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
abstractGer |
Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function is defined to penalize the distance from the desired path and the control effort. Then, the parameters of the fuzzy system are optimized for different initial and wind conditions. During the flight, the optimized fuzzy systems determine the parameters of the guidance algorithm in an online manner, in such a way that the desired path is better followed. The obtained results show that online adjustment of the PLOS guidance law improves its performance in the presence of wind by about 20%. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
abstract_unstemmed |
Abstract The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function is defined to penalize the distance from the desired path and the control effort. Then, the parameters of the fuzzy system are optimized for different initial and wind conditions. During the flight, the optimized fuzzy systems determine the parameters of the guidance algorithm in an online manner, in such a way that the desired path is better followed. The obtained results show that online adjustment of the PLOS guidance law improves its performance in the presence of wind by about 20%. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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title_short |
A Fuzzy-PLOS Guidance Law for Precise Trajectory Tracking of a UAV in the Presence of Wind |
url |
https://dx.doi.org/10.1007/s10846-022-01635-y |
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Asghari, Javad |
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up_date |
2024-07-04T01:08:28.638Z |
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