Identification of Hammerstein systems using decomposition based finite-data-window recursive least squares method with a forgetting factor

doi:10.11835/j.issn.1000-582X.2023.07.004

Home > Archive>Volume 46, Issue 7, 2023 >36-43. DOI:10.11835/j.issn.1000-582X.2023.07.004

Identification of Hammerstein systems using decomposition based finite-data-window recursive least squares method with a forgetting factor
DOI:
                        10.11835/j.issn.1000-582X.2023.07.004
                    
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Author:
                        ZHANG Yangming1ZHANG Yangming
General Key Laboratory of Complex System Simulation, Beijing 100000, P. R. China
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SU Hao2SU Hao
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
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LIU Jawei2LIU Jawei
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
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Affiliation:1.General Key Laboratory of Complex System Simulation, Beijing 100000, P. R. China;2.School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
Clc Number:N945.14
Fund Project:Supported by Natural Science Foundation of Chongqing (CSTB2022NSCQ-MSX1225), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202000602), and China Postdoctoral Science Foundation (2022MD713688).

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Abstract:

In this paper, a decomposition based recursive finite-data-window least squares identification method with a forgetting factor is proposed for Hammerstein systems. The proposed method aims to identify the parameters of Hammerstein systems by decomposing them into two subsystems, one involving linear subsystem parameters, and the other containing the nonlinear subsystem parameters. To achieve this, a two-step finite-data-window recursive least squares method with a forgetting factor is developed. To verify the effectiveness and merits of the proposed algorithm, a simulation example is provided, demonstrating that the proposed algorithm can quickly track parameters and accurately and effectively identify Hammerstein systems.

Key words:Hammerstein systems;recursive identification;least-squares method;forgetting factor

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张洋铭,苏豪,刘家尉.Hammerstein系统遗忘因子有限窗口分解辨识[J].重庆大学学报,2023,46(7):36~43

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Received:November 19,2020
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Online: August 02,2023
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