| DC Field | Value | Language |
|---|
| dc.contributor.author | Azarskov, V. N. | |
| dc.contributor.author | Zhiteckii, L. S. | |
| dc.contributor.author | Nikolaienko, S. A. | |
| dc.contributor.author | Manziuk, M. S. | |
| dc.contributor.author | Volkov, Yu. N. | |
| dc.coverage.temporal | 18–19 вересня 2018 року, Львів | |
| dc.date.accessioned | 2020-05-28T08:53:04Z | - |
| dc.date.available | 2020-05-28T08:53:04Z | - |
| dc.date.created | 2018-09-18 | |
| dc.date.issued | 2018-09-18 | |
| dc.identifier.citation | Neural 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.isbn | 978-966-941-208-9 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/50764 | - |
| dc.description.abstract | An 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.extent | 175-176 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.relation.ispartof | Автоматика/Automatiсs – 2018 : матеріали XXV Міжнародної конференція з автоматичного управління, 2018 | |
| dc.subject | aircraft | |
| dc.subject | longitudinal autopilot | |
| dc.subject | flight regime | |
| dc.subject | parameter adaptation | |
| dc.subject | neural network | |
| dc.title | Neural network approach to direct parameter adaptation of longitudinal autopilots | |
| dc.type | Article | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | National Aviation University | |
| dc.contributor.affiliation | Int. Centre of Information Technologies and Systems | |
| dc.format.pages | 2 | |
| dc.identifier.citationen | Neural 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.relation.references | 1. 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.references | 2. 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.references | 3. 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.referencesen | 1. 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.referencesen | 2. 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.referencesen | 3. 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.citation.conference | XXV Міжнародна конференція з автоматичного управління "Автоматика/Automatiсs – 2018" | |
| dc.citation.journalTitle | Автоматика/Automatiсs – 2018 : матеріали XXV Міжнародної конференція з автоматичного управління | |
| dc.citation.spage | 175 | |
| dc.citation.epage | 176 | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.subject.udc | 681.5 | |
| Appears in Collections: | Автоматика / Automatiсs. – 2018 р.
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