Hammerstein system identification in the presence of threshold-discrete Prandtl-Ishlinskii operator and its application in piezoelectric actuator

2017 ◽  
Author(s):  
Zhen Zhang ◽  
Xinyu Yang ◽  
Wentao Pang ◽  
Kemin Zhou
Keyword(s):  
System Identification ◽  
Piezoelectric Actuator ◽  
Hammerstein System

Hierarchical fractional-order Hammerstein system identification

2021 ◽  
pp. 1-13
Author(s):  
Soumaya Marzougui ◽  
Asma Atitallah ◽  
Saida Bedoui ◽  
Kamel Abderrahim
Keyword(s):  
System Identification ◽  
Fractional Order ◽  
Hammerstein System

scholarly journals Optimality analysis of the Two-Stage Algorithm for Hammerstein system identification

2009 ◽  
Vol 42 (10) ◽  
pp. 320-325
Author(s):  
Jiandong Wang ◽  
Qinghua Zhang ◽  
Lennart Ljung
Keyword(s):  
System Identification ◽  
Two Stage ◽  
Hammerstein System

scholarly journals WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
J. Zambrano ◽  
J. Sanchis ◽  
J. M. Herrero ◽  
M. Martínez
Keyword(s):  
System Identification ◽  
Evolutionary Algorithm ◽  
Nonlinear System Identification ◽  
Search Space ◽  
Single Step ◽  
Fine Tuning ◽  
Hammerstein Model ◽  
Hammerstein System ◽  
Hammerstein Systems ◽  
Novel Approach

Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analysed by using a demonstration example and a nonlinear system identification benchmark.

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