A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener Hammerstein Models by Using WH-EA and Multistep Signals

[EN] Wiener, Hammerstein, and Wiener-Hammerstein structures are useful for modelling dynamic systems that exhibit a static type nonlinearity. Many methods to identify these systems can be found in the literature; however, choosing a method requires prior knowledge about the location of the static no...

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Bibliographic Details
Authors: Zambrano-Abad, Julio Cesar, Herrero Durá, Juan Manuel|||0000-0003-1914-7494, Sanchís Saez, Javier|||0000-0001-9697-2696, Martínez Iranzo, Miguel Andrés|||0000-0002-1444-0651
Format: article
Publication Date:2020
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/204335
Online Access:https://riunet.upv.es/handle/10251/204335
Access Level:Open access
Keyword:Evolutionary algorithms
Wiener models
Hammerstein models
Wiener-hammerstein models
Non linear identification
INGENIERIA DE SISTEMAS Y AUTOMATICA
Description
Summary:[EN] Wiener, Hammerstein, and Wiener-Hammerstein structures are useful for modelling dynamic systems that exhibit a static type nonlinearity. Many methods to identify these systems can be found in the literature; however, choosing a method requires prior knowledge about the location of the static nonlinearity. In addition, existing methods are rigid and exclusive for a single structure. This paper presents a unified approach for the identification of Wiener, Hammerstein, and Wiene-Hammerstein models. This approach is based on the use of multistep excitation signals and WH-EA (an evolutionary algorithm for Wiener¿Hammerstein system identification). The use of multistep signals will take advantage of certain properties of the algorithm, allowing it to be used as it is to identify the three types of structures without the need for the user to know a priori the process structure. In addition, since not all processes can be excited with Gaussian signals, the best linear approximation (BLA) will not be required. Performance of the proposed method is analysed using three numerical simulation examples and a real thermal process. Results show that the proposed approach is useful for identifying Wiener, Hammerstein, and Wiener-Hammerstein models, without requiring prior information on the type of structure to be identified.