Identification of a Non‐Commensurate Fractional‐Order Nonlinear System Based on the Separation Scheme

ABSTRACT This article is aimed to study the parameter estimation problems of a non‐commensurate fractional‐order system with saturation and dead‐zone nonlinearity. In order to reduce the structural complexity of the system, the model separation scheme is used to decompose the fractional‐order nonlinear system into two subsystems, one includes the parameters of the linear part and the other includes the parameters of the nonlinear part. Then, we derive an auxiliary model separable gradient‐based iterative algorithm with the help of the model separation scheme. In addition, to improve the utilization of the real time information, an auxiliary model separable multi‐innovation gradient‐based iterative algorithm is presented based on the sliding measurement window. Finally, the feasibility of the presented algorithms is validated by numerical simulations.

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Publication Details

Journal
International Journal of Adaptive Control and Signal Processing
Published
2024-10-27
DOI
https://doi.org/10.1002/acs.3923
Citations
31
Primary Topic
Control Systems and Identification
Type
article
Field-Weighted Citation Impact
10.18

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Controls
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article

Identification of a Non‐Commensurate Fractional‐Order Nonlinear System Based on the Separation Scheme

Feng Ding, Junwei Wang, Weili Xiong
31 citations
International Journal of Adaptive Control and Signal Processing
Control Systems and Identification
10.18
article

Identification of a Non‐Commensurate Fractional‐Order Nonlinear System Based on the Separation Scheme

Feng Ding, Junwei Wang, Weili Xiong
article en
31 citations

Abstract

ABSTRACT This article is aimed to study the parameter estimation problems of a non‐commensurate fractional‐order system with saturation and dead‐zone nonlinearity. In order to reduce the structural complexity of the system, the model separation scheme is used to decompose the fractional‐order nonlinear system into two subsystems, one includes the parameters of the linear part and the other includes the parameters of the nonlinear part. Then, we derive an auxiliary model separable gradient‐based iterative algorithm with the help of the model separation scheme. In addition, to improve the utilization of the real time information, an auxiliary model separable multi‐innovation gradient‐based iterative algorithm is presented based on the sliding measurement window. Finally, the feasibility of the presented algorithms is validated by numerical simulations.

International Journal of Adaptive Control and Signal ProcessingVol. 39(1)
Jiangnan University (CN), Hubei University of Technology (CN)
National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 1%
Control Systems and Identification
10.18
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