首页|Aircraft parameter estimation using a stacked long short-term memory network and Levenberg-Marquardt method

Aircraft parameter estimation using a stacked long short-term memory network and Levenberg-Marquardt method

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To effectively estimate the unknown aerodynamic parameters from the aircraft's flight data,this paper proposes a novel aerodynamic parameter estimation method incorporating a stacked Long Short-Term Memory(LSTM)network model and the Levenberg-Marquardt(LM)method.The stacked LSTM network model was designed to realize the aircraft dynamics modeling by utilizing a frame of nonlinear functional mapping based entirely on the measured input-output data of the aircraft system without requiring explicit postulation of the dynamics.The LM method combines the already-trained LSTM network model to optimize the unknown aerodynamic param-eters.The proposed method is applied by using the real flight data,generated by ATTAS aircraft and a bio-inspired morphing Unmanned Aerial Vehicle(UAV).The investigation reveals that for the two different flight data,the designed stacked LSTM network structure can maintain the effi-cacy of the network prediction capability only by appropriately adjusting the dropout rates of its hidden layers without changing other network parameters(i.e.,the initial weights,initial biases,number of hidden cells,time-steps,learning rate,and number of training iterations).Besides,the proposed method's effectiveness and potential are demonstrated by comparing the estimated results of the ATTAS aircraft or the bio-inspired morphing UAV with the corresponding reference values or wind-tunnel results.

Parameter estimationLSTM network modelLM methodAerodynamic parametersFlight dataAircraft dynamics modelingNetwork prediction capabil-ityNetwork parameters

Zhe HUI、Yinan KONG、Weigang YAO、Gang CHEN

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School of Aerospace Science and Technology,Xidian University,Xi'an 710071,China

Computation Aerodynamics Institution,China Aerodynamics Research and Development Center,Mianyang 621000,China

Faculty of Computing,Engineering and Media,De Montfort University,Leicester LE1 9BH,United Kingdom

School of Aerospace Engineering,Xi'an Jiaotong University,Xi'an 710049,China

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National Natural Science Foundation of ChinaNatural Science Foundation of Shaanxi Province,ChinaFundamental Research Funds for the Central Universities,China

521926332022JC-03XJSJ23164

2024

10.1016/j.cja.2023.09.002

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(2)
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