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Actuators 2015, 4(1), 39-59;

Parameters Identification for a Composite Piezoelectric Actuator Dynamics

Department of Computer Science & Industrial Technology, Southeastern Louisiana University, Hammond, LA 70402-10847, USA
Department of Mechanical Engineering, University of Nevada, Las Vegas, NV 89154-4027, USA
Author to whom correspondence should be addressed.
Academic Editors: Mathieu Grossard and Micky Rakotondrabe
Received: 9 January 2015 / Revised: 2 March 2015 / Accepted: 11 March 2015 / Published: 17 March 2015
(This article belongs to the Special Issue High-Resolution Actuators)
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This work presents an approach for identifying the model of a composite piezoelectric (PZT) bimorph actuator dynamics, with the objective of creating a robust model that can be used under various operating conditions. This actuator exhibits nonlinear behavior that can be described using backlash and hysteresis. A linear dynamic model with a damping matrix that incorporates the Bouc–Wen hysteresis model and the backlash operators is developed. This work proposes identifying the actuator’s model parameters using the hybrid master-slave genetic algorithm neural network (HGANN). In this algorithm, the neural network exploits the ability of the genetic algorithm to search globally to optimize its structure, weights, biases and transfer functions to perform time series analysis efficiently. A total of nine datasets (cases) representing three different voltage amplitudes excited at three different frequencies are used to train and validate the model. Four cases are considered for training the NN architecture, connection weights, bias weights and learning rules. The remaining five cases are used to validate the model, which produced results that closely match the experimental ones. The analysis shows that damping parameters are inversely proportional to the excitation frequency. This indicates that the suggested hysteresis model is too general for the PZT model in this work. It also suggests that backlash appears only when dynamic forces become dominant. View Full-Text
Keywords: piezoelectric bimorph actuator; hybrid genetic algorithms-neural network; damping; Bouc–Wen hysteresis; backlash piezoelectric bimorph actuator; hybrid genetic algorithms-neural network; damping; Bouc–Wen hysteresis; backlash

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Saadeh, M.; Trabia, M. Parameters Identification for a Composite Piezoelectric Actuator Dynamics. Actuators 2015, 4, 39-59.

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