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Synthesis cascade estimation for aircraft system identification

Wang Jianhong (School of Engineering and Science, Tecnológico de Monterrey, Monterrey, Mexico)
Ricardo A. Ramirez-Mendoza (School of Engineering and Science, Tecnológico de Monterrey, Monterrey, Mexico)

Aircraft Engineering and Aerospace Technology

ISSN: 0002-2667

Article publication date: 13 June 2022

Issue publication date: 2 January 2023




The purpose of this paper extends the authors’ previous contributions on aircraft system identification, such as open loop identification or closed loop identification, to cascade system identification. Because the cascade system is one special network system, existing in lots of practical engineers, more unknown systems are needed to identify simultaneously within the statistical environment with the probabilistic noises. Consider this problem of cascade system identification, prediction error method is proposed to identify three unknown systems, which are parameterized by three unknown parameter vectors. Then the cascade system identification is transferred as one parameter identification problem, being solved by the online subgradient descent algorithm. Furthermore, the nonparametric estimation is proposed to consider the general case without any parameterized process. To make up the identification mission, model validation process is given to show the asymptotic interval of the identified parameter. Finally, simulation example confirms the proposed theoretical results.


Firstly, aircraft system identification is reviewed through the understanding about system identification and advances in control theory, then cascade system identification is introduced to be one special network system. Secondly, for the problem of cascade system identification, prediction error method and online subgradient decent algorithm are combined together to identify the cascade system with the parameterized systems. Thirdly from the point of more general completeness, another way is proposed to identify the nonparametric estimation, then model validation process is added to complete the whole identification mission.


This cascade system corresponds to one network system, existing in lots of practice, such as aircraft, ship and robot, so it is necessary to identify this cascade system, paving a way for latter network system identification. Parametric and nonparametric estimations are all studied within the statistical environment. Then research on bounded noise is an ongoing work.


To the best of the authors’ knowledge, research on aircraft system identification only concern on open loop and closed loop system identification, no any identification results about network system identification. This paper considers cascade system identification, being one special case on network system identification, so this paper paves a basic way for latter more advanced system identification and control theory.



Data availability: The data used to support the findings of this study are available from the corresponding author upon request.


Jianhong, W. and Ramirez-Mendoza, R.A. (2023), "Synthesis cascade estimation for aircraft system identification", Aircraft Engineering and Aerospace Technology, Vol. 95 No. 1, pp. 73-84.



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