Transition state performance optimization of propfan engine based on DDPG algorithm

As the “heart” of an aircraft, aero-engines work in harsh environments of high temperature and high pressure for a long time. In order to ensure that the engine can operate safely and reliably within the entire flight envelope, a large safety margin needs to be reserved in the design of the control...

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Vydané v:Journal of physics. Conference series Ročník 2472; číslo 1; s. 12010 - 12015
Hlavní autori: Zheng, Hua, Yang, Zhao-xing, Wang, Ya-fan, Zhao, Dong-zhu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.05.2023
Predmet:
Aerospace engines
Aircraft engines
Algorithms
Control systems design
Flight envelopes
High temperature
Machine learning
Optimization
Physics
Propfans
Safety margins
ISSN:1742-6588, 1742-6596
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Shrnutí:As the “heart” of an aircraft, aero-engines work in harsh environments of high temperature and high pressure for a long time. In order to ensure that the engine can operate safely and reliably within the entire flight envelope, a large safety margin needs to be reserved in the design of the control system. This design idea limits the full play of the engine performance, so it is necessary to carry out the research on the performance search control (PSC) of aero-engine. This paper studies the performance optimization control of propfan engine based on deep reinforcement learning algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm, which is more suitable for continuous action space, is used to optimize the acceleration process of the propfan engine. The simulation results show that, compared with the unoptimized adjustment process, the transition state adjustment time of the engine is reduced by 32.5% under the action of the optimal adjustment law obtained by the DDPG algorithm. Therefore, the DDPG algorithm can be applied to the performance optimization of the engine acceleration process, and has a good transition state performance optimization effect.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2472/1/012010