Hysteresis modeling and tracking control for piezoelectric stack actuators using neural-Preisach model

2019 ◽  
Vol 61 (3) ◽  
pp. 445-459
Author(s):  
Yuansheng Chen ◽  
Qinbin Huang ◽  
Hao Wang ◽  
Jinhao Qiu
Keyword(s):  
Tracking Control ◽  
Preisach Model ◽  
Hysteresis Modeling

Tracking control of piezoceramic actuators by using preisach model

2005 ◽  
Author(s):  
Xiaofeng Zhou ◽  
Shixi Yang ◽  
Guoning Qi ◽  
Xiaoping Hu
Keyword(s):  
Tracking Control ◽  
Preisach Model

Hysteresis Modeling of High-Temperature Superconductor Using Simplified Preisach Model

2015 ◽  
Vol 51 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Nana Duan ◽  
Weijie Xu ◽  
Shuhong Wang ◽  
Jianguo Zhu ◽  
Youguang Guo
Keyword(s):  
High Temperature ◽  
High Temperature Superconductor ◽  
Preisach Model ◽  
Hysteresis Modeling

Optimization of Distribution Functions for the Hysteresis Preisach Model by Genetic Algorithms

2015 ◽  
Vol 799-800 ◽  
pp. 1330-1338
Author(s):  
Mounir Boudjerda ◽  
Mounir Amir ◽  
Mourad Zergoug ◽  
Siham Azzi ◽  
Mouhamed Sahnoun
Keyword(s):  
Genetic Algorithm ◽  
Hysteresis Loop ◽  
Magnetic Hysteresis ◽  
Ferromagnetic Material ◽  
Distribution Functions ◽  
Preisach Model ◽  
Wide Range ◽  
Material Behaviors ◽  
Hysteresis Modeling

The description of hysteresis is one of the classical problems in magnetic materials. The progress in its solution determines the reliability of modeling and the quality of design of a wide range of contemporary devices, as well as devices that will be created in the future. The intensive investigations in hysteresis modeling were induced by the fact that accuracy models of magnetic hysteresis must be studied yet. In this paper, several identification procedures of the distribution functions of the Preisach model will be investigated by means of a genetic algorithm.The proposed approach has been applied to model the behavior of many samples and distribution functions are optimized which will give accurate results of the hysteresis loop. The results show the robustness and efficiency of genetic algorithm to model the phenomenon of hysteresis loop. This work can give solutions about the ferromagnetic material evaluations and shows the optimization of distribution functions according to the material behaviors.

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