Intelligent control for Hammerstein nonlinear systems with arbitrary deadzone input

Zhang, Bi; Mao, Zhizhong; Zhang, Tingfeng

doi:10.1177/0142331215611934

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…One class of such nonlinear modeling system is so-called block-oriented models that can be represented in various configurations where linear dynamic blocks and nonlinear static or dynamic subsystems are cascaded. e Hammerstein (H) model (static nonlinear block followed by a dynamic linear one) and the Wiener (W) system (a linear dynamic subsystem followed by a static nonlinear block) are the basic class of the cascaded systems which are widely used in many industrial practice engineering applications [14][15][16][17][18][19][20][21][22] and, therefore, the modeling approaches of such class of block-oriented models have received great attention for many years [23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Hammerstein and Wiener systems are combined together to produce more complex subcategories, namely, Hammerstein-Wiener (HW) model (a linear block is cascaded between two nonlinear subsystems) and a Wiener-Hammerstein (WH) system (a nonlinear block is embedded between two linear blocks).…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

et al. 2021

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This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output-error autoregressive (OEA) noise. Two fuzzy systems are suggested for the identification of the input and output nonlinear blocks of a proposed model from given input-output data measurements. In this work, the need for the commonly used assumptions including well-known structure of input and/or output nonlinearities and/or reversible nonlinear output is eliminated by replacing the intermediate variables and noise with their estimates. Four parametric estimation algorithms to identify the proposed fuzzy-type stochastic output-error autoregressive HW (FSOEAHW) model are derived based on backpropagation algorithm and multi-innovation and data filtering identification techniques. The proposed algorithms are improved backpropagation gradient (IBPG) algorithm, multi-innovation IBPG (MIIBPG) algorithm, a data filtering IBPG (FIBPG) algorithm, and a multi-innovation-based FIBPG (MIFIBPG) algorithm. The convergence of the parameter estimation algorithms is studied. The effectiveness of the proposed algorithms is shown by a given simulation example.

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Section: Introductionmentioning

confidence: 99%

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

et al. 2021

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“…Se comparadas aos modelos de Volterra e bilinear, as estruturas de Hammerstein e de Wiener apresentam como vantagem adicional a simplicidade das suas representações (Haryanto e Hong, 2013;Mzyk, 2014). Além disso, tais estruturas foram usadas com sucesso para representar sistemas não lineares em diversas aplicações práticas na área de processos químicos (Zou et al, 2015), biológicos (Jalaleddini e Kearney, 2013;Abedini Najafabadi e Shahrokhi, 2016;Narayanan et al, 2017) e de controle (Gao et al, 2015;Zhang et al, 2017).…”

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Nova Abordagem para Linearização de Modelos de Hammerstein Identificados por Métodos de Subespaço

Santos

Braga

Ricco

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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The quality of the model is an impacting factor on the accuracy and response speed of a control system. To maintain the trade-off between model and controller complexity, the use of interconnected blocks in the Hammerstein structure is an interesting alternative. The motivation is that linearizing such models allows the use of classical control techniques in a wider range of process operation. After several tests performed in this work, it was observed that static functional functions in small process operating ranges are not effective as described in the literature. Therefore, this paper proposes, by means of algebraic manipulations, the use of an algebraic inverse of the static curve estimated for the Hammerstein model. In the first approach, the static curve and the linear block are estimated in one step. In the second, each of the plots is estimated in two steps. The numerical results suggest that the algebraic inverse coupled with the two-step methodology provides greater accuracy in control, however a detailed feasibility analysis should be done in each case. Resumo: A qualidade do modelo é um fator impactante na precisão e velocidade de resposta de um sistema de controle. Para manter o compromisso entre a complexidade do modelo e do controlador, o uso de blocos interconectados na estrutura de Hammerstein é uma alternativa interessante. A motivação é que linearizar tais modelos permite o uso de técnicas clássicas de controle em uma faixa mais ampla de operação do processo. Após vários testes realizados neste trabalho, observou-se que funções estáticas funcionais em pequenas faixas de operação do processo não são eficazes como descrito na literatura. Sendo assim, este trabalho propõe, por meio de manipulações algébricas, o uso de uma inversa algébrica da curva estática estimada para o modelo de Hammerstein. Na primeira abordagem, a curva estática e o bloco linear são estimados em uma etapa. Na segunda, estima-se cada uma das parcelas em duas etapas. Os resultados numéricos sugerem que a inversa algébrica aliada à metodologia em duas etapas proporcionam maior precisão no controle, todavia uma análise detalhada de viabilidade deve ser feita em cada caso.

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Parameter Estimation for Control of Hammerstein Systems with Dead-Zone Nonlinearity

Ren

2018

Lecture Notes in Electrical Engineering

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Intelligent control for Hammerstein nonlinear systems with arbitrary deadzone input

Cited by 4 publications

References 33 publications

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

Nova Abordagem para Linearização de Modelos de Hammerstein Identificados por Métodos de Subespaço

Parameter Estimation for Control of Hammerstein Systems with Dead-Zone Nonlinearity

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