Online from: 1984
Subject Area: Mechanical & Materials Engineering
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|Title:||Neural network hysteresis modeling with an improved Preisach model for piezoelectric actuators|
|Author(s):||Weiping Guo, (Institute of Computer Science and Technology, Yantai University, Yantai City, People's Republic of China), Diantong Liu, (Institute of Computer Science and Technology, Yantai University, Yantai City, People's Republic of China), Wei Wang, (China Astronautics Standards Institute, Beijing City, People's Republic of China)|
|Citation:||Weiping Guo, Diantong Liu, Wei Wang, (2012) "Neural network hysteresis modeling with an improved Preisach model for piezoelectric actuators", Engineering Computations, Vol. 29 Iss: 3, pp.248 - 259|
|Keywords:||Hysteresis, Neural network modeling, Piezoelectric actuator, Preisach model|
|Article type:||Research paper|
|DOI:||10.1108/02644401211212389 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||This work is supported by the National Natural Science Foundation of China (No. 61175086) and the Research Award Fund for Outstanding Middle-aged and Young Scientist of Shandong Province (No. BS2009DX021).|
Purpose – Widely used in micro-position devices and vibration control, the piezoelectric actuator exhibits strong hysteresis effects, which can cause inaccuracy and oscillations, even lead to instability. If the hysteretic effects can be predicted, a controller can be designed to correct for these effects. This paper aims to present a neural network hysteresis model with an improved Preisach model to predict the output of piezoelectric actuator.
Design/methodology/approach – The improved Preisach model is given: A wiping-out memory sequence is defined that is along a single axis only and at the same time the ascending and the descending extreme points are separated. The extended area variable is calculated according to wiping-out memory sequence. The relationship between the two inputs (the extended area variable and variable rate of input signal) and the hysteresis output is implemented with a neural network to approximate the hysteresis model for the piezoelectric actuators.
Findings – Some experiments are carried out with a piezoelectric ceramic (PST150/7/40 VS12) and the results show the neural network hysteresis model can reliably predict the hysteretic behaviours in piezoelectric actuators.
Originality/value – The improved Preisach model is a simple model that is implemented by a neural network to reliably predict the hysteretic output in piezoelectric actuators.
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