Hysteresis Identification of Piezoelectric Actuator Using Neural Network Trained By Jaya Algorithm

Sơn, Nguyễn Ngọc; Ánh, Hồ Phạm Huy; Kien, Cao Van; Chinh, Tran Minh

doi:10.1109/isee2.2019.8920963

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Using the second type of hysteresis model-free method, hysteresis modeling can be trained using data-driven algorithms [32]. For example, the Radial Basis Function (RBF) neural network was proposed for application in the nonlinear system [33], which incorporated a dynamic RBF neural network to model the hysteresis of the plant.…”

Section: Introductionmentioning

confidence: 99%

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

Han,

Xu,

et al. 2023

Preprint

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It is widely known that inchworm piezo motors are being used in ultra-precision detection platforms where high clamping force and nano-precision are required. This paper examines and proposes a dynamic model of the inchworm piezo motor to improve the performance and controller. Traditional identification strategies have not been adopted for calculating parameters for the dynamic model exhibiting hysteresis nonlinearity. In addition, hysteresis deteriorates the accuracy of positioning of inchworm piezo motors. It has been proposed to solve these problems using a feedforward controller, a hysteresis compensator based on a stochastic configuration networks algorithm (SCNs). Using the SCNs algorithm, both voltage and displacement inputs can be used to describe the nonlinear memory of hysteresis, and the kernel function provides a flexible and accurate representation of this behavior. This method enables the dynamic model parameters of the inchworm piezo motor to be identified using frequency sweeps. A proportional-integral (PI) controller is used to verify the positioning accuracy, and experimental results show that an inchworm piezo motor has a positioning accuracy of about 1nm when the laser interference is low with a 10M Hz adoption rate and 0.1nm resolution.

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Section: Introductionmentioning

confidence: 99%

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

Han,

Xu,

et al. 2023

Preprint

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“…The use of NN to identify material properties has been reported by several researchers [ 14 , 15 , 16 ]. In the case of piezoelectric materials, the use of NN is reported to identify nonlinearities as hysteresis and the behavior close to the resonance of a flexural actuator [ 17 , 18 , 19 ]. However, we have no references regarding the use of this technique to solve the problem of identifying the parameters in the piezoelectric model.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Identification of Piezoelectric Properties

Castillo

Pérez

2021

Materials

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The behavior of a piezoelectric element can be reproduced with high accuracy using numerical simulations. However, simulations are limited by knowledge of the parameters in the piezoelectric model. The identification of the piezoelectric model can be addressed using different techniques but is still a problem for manufacturers and end users. In this paper, we present the use of a machine learning approach to determine the parameters in the model. In this first work, the main sensitive parameters, c11, c13, c33, c44 and e33 were predicted using a neural network numerically trained by using finite element simulations. Close to one million simulations were performed by changing the value of the selected parameters by ±10% around the starting point. To train the network, the values of a PZT 27 piezoelectric ceramic with a diameter of 20 mm and thickness of 2 mm were used as the initial seed. The first results were very encouraging, and provided the original parameters with a difference of less than 0.6% in the worst case. The proposed approach is extremely fast after the training of the neural network. It is suitable for manufacturers or end users that work with the same material and a fixed number of geometries.

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Adaptive Fuzzy Sliding Mode Controller for Ball and Plate System Optimizing by Advanced Jaya Algorithm

Kien

Sơn

Ánh

2022

Lecture Notes in Mechanical Engineering

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Hysteresis Identification of Piezoelectric Actuator Using Neural Network Trained By Jaya Algorithm

Cited by 3 publications

References 24 publications

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

Machine Learning Identification of Piezoelectric Properties

Adaptive Fuzzy Sliding Mode Controller for Ball and Plate System Optimizing by Advanced Jaya Algorithm

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