Neural network approach to direct parameter adaptation of longitudinal autopilots

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dc.citation.conferenceXXV Міжнародна конференція з автоматичного управління "Автоматика/Automatiсs – 2018"
dc.citation.epage176
dc.citation.journalTitleАвтоматика/Automatiсs – 2018 : матеріали XXV Міжнародної конференція з автоматичного управління
dc.citation.spage175
dc.contributor.affiliationNational Aviation University
dc.contributor.affiliationInt. Centre of Information Technologies and Systems
dc.contributor.authorAzarskov, V. N.
dc.contributor.authorZhiteckii, L. S.
dc.contributor.authorNikolaienko, S. A.
dc.contributor.authorManziuk, M. S.
dc.contributor.authorVolkov, Yu. N.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.coverage.temporal18–19 вересня 2018 року, Львів
dc.date.accessioned2020-05-28T08:53:04Z
dc.date.available2020-05-28T08:53:04Z
dc.date.created2018-09-18
dc.date.issued2018-09-18
dc.description.abstractAn improvement of longitudinal autopilots consisting of the digital PI and P controllers is addressed in this paper. In order to achieve a good performance of these autopilots a direct adaptation of their three parameters is proposed. To this end, the two-circuit feedback is added by the feedforward circuit containing a neural network which needs to be trained offline. The input signals of this neural network correspond to the airspeed and the altitude of an aircraft whereas its output signals are the three controller parameters to be adjusted if flight regime changes. The behavior of a new longitudinal autopilot is studied by simulation experiments.
dc.format.extent175-176
dc.format.pages2
dc.identifier.citationNeural network approach to direct parameter adaptation of longitudinal autopilots / V. N. Azarskov, L. S. Zhiteckii, S. A. Nikolaienko, M. S. Manziuk, Yu. N. Volkov // Автоматика/Automatiсs – 2018 : матеріали XXV Міжнародної конференція з автоматичного управління, 18–19 вересня 2018 року, Львів. — Львів : Видавництво Львівської політехніки, 2018. — С. 175–176. — (Controlling the aerospace craft, marine vessels and other moving objects).
dc.identifier.citationenNeural network approach to direct parameter adaptation of longitudinal autopilots / V. N. Azarskov, L. S. Zhiteckii, S. A. Nikolaienko, M. S. Manziuk, Yu. N. Volkov // Avtomatyka/Automatiss – 2018 : materialy XXV Mizhnarodnoi konferentsiia z avtomatychnoho upravlinnia, 18–19 veresnia 2018 roku, Lviv. — Lviv : Vydavnytstvo Lvivskoi politekhniky, 2018. — P. 175–176. — (Controlling the aerospace craft, marine vessels and other moving objects).
dc.identifier.isbn978-966-941-208-9
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/50764
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.relation.ispartofАвтоматика/Automatiсs – 2018 : матеріали XXV Міжнародної конференція з автоматичного управління, 2018
dc.relation.references1. Zhiteckii L.S., Azarskov V.N., Pilchevsky A.Yu., Solovchuk K.Yu. Design of digital autopilot for lateral motion control of an aircraft based on l1-optimization approach. Int. Journal of Engineering Research and Application, 2016. Vol. 6, P. 70–79.
dc.relation.references2. Azarskov, V. N., Kucherov, D. P., Nikolaienko, S. A., Zhiteckii, L. S. Asymptotic behavior of gradient learning algorithms in neural network models for the identification of nonlinear systems. American Journal of Neural Networks and Applications, 2015. No 1, P. 1–10.
dc.relation.references3. Zhiteckii, L.S., Azarskov, V.N., Nikolaienko, S.A., Solovchuk, K.Yu. Some features of neural networks as nonlinearly parameterized models of unknown systems using an online learning algorithm. Journal of Applied Math. and Physics, 2018. Vol.6, No.1, P. 247–263.
dc.relation.referencesen1. Zhiteckii L.S., Azarskov V.N., Pilchevsky A.Yu., Solovchuk K.Yu. Design of digital autopilot for lateral motion control of an aircraft based on l1-optimization approach. Int. Journal of Engineering Research and Application, 2016. Vol. 6, P. 70–79.
dc.relation.referencesen2. Azarskov, V. N., Kucherov, D. P., Nikolaienko, S. A., Zhiteckii, L. S. Asymptotic behavior of gradient learning algorithms in neural network models for the identification of nonlinear systems. American Journal of Neural Networks and Applications, 2015. No 1, P. 1–10.
dc.relation.referencesen3. Zhiteckii, L.S., Azarskov, V.N., Nikolaienko, S.A., Solovchuk, K.Yu. Some features of neural networks as nonlinearly parameterized models of unknown systems using an online learning algorithm. Journal of Applied Math. and Physics, 2018. Vol.6, No.1, P. 247–263.
dc.rights.holder© Національний університет “Львівська політехніка”, 2018
dc.subjectaircraft
dc.subjectlongitudinal autopilot
dc.subjectflight regime
dc.subjectparameter adaptation
dc.subjectneural network
dc.subject.udc681.5
dc.titleNeural network approach to direct parameter adaptation of longitudinal autopilots
dc.typeArticle

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