Physics-based modelling of a piezoelectric actuator using genetic algorithm

A number of models have been presented to estimate the displacement of piezoelectric actuators; these models remove the need for accurate displacement sensors used in nanopositioning. Physics based models, inspired by physical phenomena, are widely used for this purpose due to their accuracy and comparatively low number of parameters. The common issue of these models is the lack of a non-ad-hoc and reliable method to estimate their parameters. Parameter identification of a widely accepted physics-based model, introduced by Voigt, is addressed in this paper. Non-linear governing equation of this model consists of five parameters needing to be identified. This research aims at developing/adopting an optimal and standard (non-ad-hoc) parameter identification algorithm to accurately determine the parameters of the model and, in a more general view, all physics-based models of piezoelectric actuators. In this paper, Genetic Algorithm (GA) which is a global optimisation method is employed to identify the model parameters.

[1]  Peter J. Fleming,et al.  The MATLAB genetic algorithm toolbox , 1995 .

[2]  Ben S. Cazzolato,et al.  A review, supported by experimental results, of voltage, charge and capacitor insertion method for driving piezoelectric actuators , 2010 .

[3]  Robert J. Wood,et al.  Nonlinear Performance Limits for High Energy Density Piezoelectric Bending Actuators , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[4]  Steven Grainger,et al.  A comparative study on the use of black box modelling for piezoelectric actuators , 2012 .

[5]  C. Quate,et al.  Parallel atomic force microscopy using cantilevers with integrated piezoresistive sensors and integrated piezoelectric actuators , 1995 .

[6]  Lei Chen,et al.  A comparative study of different physics-based approaches to modelling of piezoelectric actuators , 2013, 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[7]  T.-J. Yeh,et al.  An integrated physical model that characterizes creep and hysteresis in piezoelectric actuators , 2008, Simul. Model. Pract. Theory.

[8]  Micky Rakotondrabe,et al.  First experiments on MagPieR: A planar wireless magnetic and piezoelectric microrobot , 2011, 2011 IEEE International Conference on Robotics and Automation.

[9]  Sushant M. Dutta.  Dynamic hysteresis modeling and applications , 2004 .

[10]  Wonkyu Moon,et al.  Sensorless control for hysteresis compensation of AFM scanner by modified Rayleigh model , 2010 .

[11]  Mitsuo Gen,et al.  Genetic Algorithms in Optimization , 2012 .

[12]  M. Mohammadzaheri,et al.  Intelligent modeling of a piezoelectric tube actuator , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[13]  Adrian Hopgood.  Genetic Algorithms for Optimization , 2011 .

[14]  William W. Clark,et al.  Piezoelectric Energy Harvesting with a Clamped Circular Plate: Analysis , 2005 .

[15]  Steven Grainger,et al.  Fuzzy modeling of a piezoelectric actuator , 2012 .

[16]  Abdollah Homaifar,et al.  Constrained Optimization Via Genetic Algorithms , 1994, Simul..

[17]  Mohammed Douimi,et al.  Piezo-actuators modeling for smart applications , 2011 .

[18]  Christopher J Murphy,et al.  The applications of atomic force microscopy to vision science. , 2010, Investigative ophthalmology & visual science.

[19]  Claudiu Valentin Suciu,et al.  Modeling and Simulation of a Vehicle Suspension with Variable Damping and Elastic Properties versus the Excitation Frequency , 2011, 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[20]  W. Messner,et al.  Piezoelectric microactuator for dual stage control , 1999 .