NDLI: An aircraft's parameter identification algorithm based on cloud model optimization

Content Provider	IEEE Xplore Digital Library
Author	Wei Zhang Yi-lei Liu Da-Peng Guo Masood, K. Jing Tian
Copyright Year	2014
Description	Author affiliation: Aerodynamic Dev. Dept., AVIC Aerodynamics Res. Inst., Shenyang, China (Da-Peng Guo) \|\| Sch. of Aeronaut., Northwestern Polytech. Univ., Xi'an, China (Wei Zhang; Yi-lei Liu; Masood, K.; Jing Tian)
Abstract	The maximum likelihood (ML) estimation method has been extensively applied to identifying the parameters of an aircraft. But it has to derive sensitivity equations in advance and solve sensitivity matrices, thus being complicated for its application and easily reaching locally optimal solutions. The paper proposes an aircraft's parameter identification algorithm, which optimizes the ML function with the cloud model optimization theory in accordance with the ML estimation principle, thus obtaining the values of the parameters to be identified. The algorithm does not have to derive sensitivity matrices, has no high requirements for initial values and is little affected by noise. Thus it is easy to apply, can be optimized by the cloud model and have rather fast convergence and nice global search capability and thus not easily reaching locally optimal solutions. The Twin Otter airplane is used as a numerical example to verify the algorithm. The numerical results show that the parameter identification algorithm is easy to implement, has good identification precision and fast convergence and does not reach locally optimal solutions.
Starting Page	1101
Ending Page	1106
File Size	1548893
Page Count	6
File Format	PDF
ISSN	20937121
e-ISBN	9788993215076
DOI	10.1109/ICCAS.2014.6987544
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-22
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Control, Robotics and Systems (ICROS)
Subject Keyword	Integrated circuits Cloud model Maximum likelihood (ML) estimation Noise Vectors Noise measurement Aircraft parameter identification Niobium Optimization Intelligent control Convergence
Content Type	Text
Resource Type	Article

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