Neural network hysteresis modeling with an improved Preisach model for piezoelectric actuators

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 e...

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Bibliographic Details
Published in:Engineering computations 2012-01, Vol.29 (3), p.248-259
Main Authors: Guo, Weiping, Liu, Diantong, Wang, Wei
Format: Article
Language:English
Subjects:
Approximation
Experiments
Hysteresis
Instability
Mathematical models
Microelectronics
Neural networks
Oscillations
Piezoelectric actuators
Piezoelectric ceramics
Studies
Vibration
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Summary: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.
ISSN:0264-4401
1758-7077
DOI:10.1108/02644401211212389