Subspace Model Identification Method for Flight Dynamics of FixedWing Airplane

Abstract

Abstract: Abstract： Due to notable advantages of simplicity and high efficiency, the subspace model identification (SMI) method becomes an attractive modeling approach for flight dynamics of the airplane system. However, because of the nonlinearity in the airplane system, SMI based model identification approaches which are designed for linear systems always generate some modeling errors. To deal with this problem, a novel twostage system identification approach for flight dynamics of the fixedwing airplane system was proposed. An instrumental variable aided closedloop SMI was first adopted to obtain the approximate linear model of the airplane dynamics. Then, an extended state observer (ESO) able to estimate the nonlinear dynamics of the airplane system was constructed using the obtained linear model. Based on the estimates by the ESO, a group of neural networks were trained to identify the decentralized model of the system nonlinearity. Finally, the nonlinear, six degreeoffreedom (6DOF) model of a B747 airplane was applied to system identification test, and the results obtained verify the effectiveness of the proposed approach.

Key words: Key words： subspace model identification（SMI）, fixedwing airplane, instrumental variable, extended state observer（ESO）, neural network

CLC Number:

V 211
TP 13

QI Xiaohui1,WANG Jianchen2. Subspace Model Identification Method for Flight Dynamics of FixedWing Airplane[J]. Journal of Shanghai Jiaotong University, 2016, 50(04): 613-618.

References

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