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Direct Thrust Inverse Control of Aero-Engine Based on Deep Neural Network

  • Qiangang Zheng EMAIL logo , Ziyan Du , Dawei Fu , Zhongzhi Hu and Haibo Zhang
Published/Copyright: January 22, 2019
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Abstract

A novel thrust control method based on inverse control is proposed to improve engine response ability. The On Line Sliding Window Deep Neural Network (OL SW DNN) is proposed and adopted as inverse mapping model modeling method of inverse control. The OL SW DNN has deeper layer structure, which makes the inverse mapping model have stronger fitting capacity for nonlinear object than traditional NN. Moreover, due to adopt on-line learning modeling method, the proposed adaptive control method can obtain desired thrust whether engine degrades or not. The comparison simulations of the traditional control method based on PID and the proposed control method are carried out. Compared with the traditional control method, the proposed control method can obtain desired thrust when the engine degradation occurs, but also has fast response ability (the acceleration times for engine thrust increase to 95 % thrust of acceleration object decreases by 1.35 seconds).

Funding statement: This was supported in part by the National Natural Science Foundation of China under Grant 51576096, in part by Q. Lan and the 333 Project, in part by the Research Funds for Central Universities under Grant NF2018003, in part by Six Talents Peak Project of Jiangsu Province.

Nomenclature

Symbol

Explanation

H

Height

Ma

Mach number

PLA

Power level angle

Wfb

Fuel flow

F

Engine thrust

T41

High pressure turbine inlet temperature

Nf

Fan rotor speed

Nc

Compressor rotor speed

Smf

Fan surge margin

Smc

Compressor surge marge

Ps3

Combustor inlet pressure

RU

Ratio Unit (Wfb /Ps3 )

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Received: 2018-12-23
Accepted: 2019-01-13
Published Online: 2019-01-22
Published in Print: 2021-12-20

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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