Abstract
Problem statement: Piezoelectric actuator is a kind of key driving components for micropositioning stages, micropumps, micro valves, micro gripper and so on in the fields of micro/nano technology such as integrated circuit manufacturing, precision instruments, ultra precision fabrication, biomedical manipulation. It has lots of advantages including high stiffness, fast response times, less heat generating, low power consumption and large force output. But the hysteresis nonlinearity seriously affects working performance of actuators. So a lot of models were proposed to describe the hysteresis nonlinearity. A popular model which was widely used is the Preisach model. In order to obtain accurate displacement output corresponding to arbitrary input voltage with the Preisach model, function output approximation is needed. Approach: In this study, firstly the Preisach model was introduced. Then the function modeling of Preisach model based on a Bayesian Regularization Back Propagation Neural (BRBPNN) was presented and a three layers BPNN was designed. Finally, the BRBPNN was trained in Neural Network toolbox of MATLAB6.0. The Preisach function values not at equal diversion points were calculated by the trained network and the actual displacement outputs and theoretical values corresponding to random voltages input were compared. Results: Experimental results indicate that theoretical displacements and measured displacements agree with very well, the maximum displacement error is 0.35μm and the standard deviation is 0.24 μm. Conclusion: The BRBPNN could realize function approximation in Preisach modeling accurately and could meet the precision requirement in the field of modeling and controlling of piezoelectric actuators.
Highlights
Diversion points of voltage directly affects the output accuracy
Piezoelectric actuators are widely used in ultra- the number of equal diversion points are relatively less, precision machining, integrated circuit manufacturing, but as the increasing of equal diversion points, the precision instruments (Wang et al, 2010) due to amount of experimental data which needed to be advantages of high displacement resolution, fast acquired will grow dramatically
The Preisach model is a hysteresis model function approximation. It could reduce the influence of describing the static nonlinearity of piezoelectric hysteresis and effectively improve the control precision actuators, which was proposed by Preisach (1935) of the piezoelectric actuator
Summary
Diversion points of voltage directly affects the output accuracy. The model has a relatively great error when. The traditional method of function approximation using Preisach model to compute displacement output includes Bilinear Interpolation, Polynomial Fitting but of a piezoelectric actuator, the density level of the equal the approximation accuracy is not high enough for Corresponding Author: Wen Wang, Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, P.R. China Fax: +86-571-87951906. Some researchers used neural network to model the hysteresis curve directly They used different neural networks to fit ascending and descending curves respectively, but when the change history of the input voltage is different from one of the experimental data used in training, the output cannot be predicted by the trained neural networks (Hwang et al, 2001). This study utilizes BPNN to realize function approximation based on Preisach model and select Bayesian Regularization method to optimize, which has a marked effect on enhancing generalization ability of network and increasing training speed and has been used in some research works (Aggarwal et al, 2005). When u(t) is on the upswing and the decline, the model is Eq 3-4, respectively:
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
