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A Research on Aero-engine Control Based on Deep Q Learning

  • Qiangang Zheng EMAIL logo , Zhihua Xi , Chunping Hu , Haibo ZHANG und Zhongzhi Hu
Veröffentlicht/Copyright: 21. April 2020
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Abstract

For improving the response performance of engine, a novel aero-engine control method based on Deep Q Learning (DQL) is proposed. The engine controller based on DQL has been designed. The model free algorithm – Q learning, which can be performed online, is adopted to calculate the action value function. To improve the learning capacity of DQL, the deep learning algorithm – On Line Sliding Window Deep Neural Network (OL-SW-DNN), is adopted to estimate the action value function. For reducing the sensitivity to the noise of training data, OL-SW-DNN selects nearest point data of certain length as training data. Finally, the engine acceleration simulations of DQR and the Proportion Integration Differentiation (PID) which is the most commonly used as engine controller algorithm in industry are both conducted to verify the validity of the proposed method. The results show that the acceleration time of the proposed method decreased by 1.475 second while satisfied all of engine limits compared with the tradition controller.

Funding statement: This study was supported in part by National Natural Science Foundation of China under Grant 51906102, in part by National Science and Technology Major Project under Grant 2017-V-0004-0054, in part by Research on the Basic Problem of Intelligent Aero-engine under Grant 2017-JCJQ-ZD-047-21, in part by China Postdoctoral Science Foundation Funded Project under Grant 2019M661835, in part by Aeronautics Power Foundation under Grant 6141B09050385, in part by the Fundamental Research Funds for the Central Universities under Grant NT2019004.

Nomenclature

Symbol Explanation Symbol Explanation
H Height T41 High pressure turbine inlet temperature
Ma Mach number Nf Fan rotor speed
PLA Power level angle Nc Compressor rotor speed
Wfb Fuel flow Smf Fan surge margin
F Engine thrust Smc Compressor surge marge

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Received: 2020-03-13
Accepted: 2020-03-31
Published Online: 2020-04-21
Published in Print: 2022-12-16

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