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Design and evaluation of advanced intelligent flight controllers

Milz, Daniel und Looye, Gertjan (2020) Design and evaluation of advanced intelligent flight controllers. In: AIAA Scitech 2020 Forum. AIAA Scitech 2020 Forum, 2020-01-06 - 2020-01-10, Orlando, FL. doi: 10.2514/6.2020-1846. ISBN 978-162410595-1.

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Offizielle URL: https://arc.aiaa.org/doi/abs/10.2514/6.2020-1846

Kurzfassung

Reinforcement learning based methods could be feasible of solving adaptive optimal control problems for nonlinear dynamical systems. This work presents a proof of concept for applying reinforcement learning based methods to robust and adaptive flight control tasks. A framework for designing and examining these methods is introduced by means of the open research civil aircraft model (RCAM) and optimality criteria. A state-of-the-art robust flight controller - the incremental nonlinear dynamic inversion (INDI) controller - serves as a reference controller. Two intelligent control methods are introduced and examined. The deep deterministic policy gradient (DDPG) controller is selected as a promising actor critic reinforcement learning method that currently gains much attraction in the field of robotics. In addition, an adaptive version of a proportional-integral-derivative (PID) controller, the PID neural network (PIDNN) controller, is selected as the second method. The results show that all controllers are able to control the aircraft model. Moreover, the PIDNN controller exhibits improved reference tracking if a good initial guess of its weights is available. In turn, the DDPG algorithm is able to control the nonlinear aircraft model while minimizing a multi-objective value function. This work provides insight into the usability of selected intelligent controllers as flight control functions as well as a comparison to state-of-the-art flight control functions.

elib-URL des Eintrags:https://elib.dlr.de/139143/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Design and evaluation of advanced intelligent flight controllers
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Milz, DanielDaniel.Milz (at) dlr.dehttps://orcid.org/0000-0001-9704-2036133675462
Looye, GertjanGertjan.Looye (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:5 Januar 2020
Erschienen in:AIAA Scitech 2020 Forum
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Nein
DOI:10.2514/6.2020-1846
ISBN:978-162410595-1
Status:veröffentlicht
Stichwörter:Flight Control, Reinforcement Learning, Artificial Intelligence, Machine Learning, INDI
Veranstaltungstitel:AIAA Scitech 2020 Forum
Veranstaltungsort:Orlando, FL
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:6 Januar 2020
Veranstaltungsende:10 Januar 2020
Veranstalter :AIAA
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Luftfahrt
HGF - Programmthema:Flugzeuge
DLR - Schwerpunkt:Luftfahrt
DLR - Forschungsgebiet:L AR - Aircraft Research
DLR - Teilgebiet (Projekt, Vorhaben):L - Systeme und Kabine (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Systemdynamik und Regelungstechnik > Flugzeug-Systemdynamik
Hinterlegt von: Milz, Daniel
Hinterlegt am:07 Dez 2020 17:46
Letzte Änderung:24 Apr 2024 20:40

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