Fractional-order Hammerstein state-space modeling of nonlinear dynamic systems from input-output measurements

被引：15

作者：

Rahmani, Mohammad-Reza ^{[1
]}

Farrokhi, Mohammad ^{[2
]}

机构：

[1] Iran Univ Sci & Technol, Sch Elect Engn, Tehran 1684613114, Iran

[2] Iran Univ Sci & Technol, Ctr Excellence Modeling & Control Complex Syst, Sch Elect Engn, Tehran 1684613114, Iran

来源：

ISA TRANSACTIONS | 2020年 / 96卷

关键词：

Hammerstein model; State estimation; Fractional-order systems; Neural networks; IDENTIFICATION METHOD; PARAMETER-ESTIMATION; LYAPUNOV FUNCTIONS; STABILITY;

D O I：

10.1016/j.isatra.2019.06.015

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper introduces a continuous-time fractional-order Hammerstein state-space model with a systematic identification algorithm for modeling nonlinear dynamic systems. The proposed model consists of a radial-basis function neural network followed by a fractional-order system. The proposed identification scheme is accomplished in two stages. The structural parameters of the fractional-order system (i.e. the values of the fractional order and the degree of the denominator in the fractional order system) are estimated in the frequency domain. Then, the synaptic weights of the radial-basis function neural network and the coefficients of the fractional-order system are determined in the time domain via the Lyapunov stability theory, which guarantees stability of the given method and its convergence under a mild condition. Three examples are provided to show the effectiveness of the proposed method. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：177 / 184

页数：8

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