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

2012 ◽  
Vol 29 (3) ◽  
pp. 248-259 ◽  
Author(s):  
Weiping Guo ◽  
Diantong Liu ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Piezoelectric Actuators ◽  
Preisach Model ◽  
Hysteresis Modeling

A Novel Composite Neural Network for Hysteresis Modeling in Piezoelectric Actuators

2009 ◽  
Author(s):  
Mohamad Fazli ◽  
Seyed Mahdi Rezaei ◽  
Mohamad Zareienejad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Piezoelectric Actuators ◽  
Maximum Error ◽  
Feed Forward ◽  
Positioning Systems ◽  
Time Dynamic ◽  
Operational Conditions ◽  
Hysteresis Modeling ◽  
Feed Forward Neural Networks

Piezoelectric actuators are convenient for micro positioning systems. Inherent hysteresis is one of the drawbacks in use of these actuators. Precise control of this actuator under changing of environmental and operational conditions, without modeling of hysteresis, is impossible. Neural networks can be used for this modeling. The ordinary feed forward neural networks can not train time dynamic relationship between input and output. Thus a certain type of networks called time delay feed forward neural networks (TDNN), are developed and is used in this paper. In the previous researches in this field, the important effect of loaded force on the actuator is ignored. This can increase the positioning error remarkably. Especially when these actuators are used in the precise grinding or machining operations. In this paper, neural network is used for hysteresis modeling with attention to the important effect of loaded force. After modeling, inverse hysteresis model is used as compensator in a feed forward way to linearize the input-output relationship. Then using PI closed loop controller and selecting suitable coefficient for it, the maximum error was decreased to less than 2 percent of the working amplitude.

scholarly journals Hysteresis Modeling and Compensation of Fast Steering Mirrors with Hysteresis Operator Based Back Propagation Neural Networks

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 732
Author(s):  
Kairui Cao ◽  
Guanglu Hao ◽  
Qingfeng Liu ◽  
Liying Tan ◽  
Jing Ma
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Hysteresis Model ◽  
Cooperative Movement ◽  
Hysteresis Operator ◽  
The Neural Network ◽  
Hysteresis Nonlinearity ◽  
Hysteresis Modeling ◽  
Hysteresis Characteristics ◽  
Inverse Hysteresis

Fast steering mirrors (FSMs), driven by piezoelectric ceramics, are usually used as actuators for high-precision beam control. A FSM generally contains four ceramics that are distributed in a crisscross pattern. The cooperative movement of the two ceramics along one radial direction generates the deflection of the FSM in the same orientation. Unlike the hysteresis nonlinearity of a single piezoelectric ceramic, which is symmetric or asymmetric, the FSM exhibits complex hysteresis characteristics. In this paper, a systematic way of modeling the hysteresis nonlinearity of FSMs is proposed using a Madelung’s rules based symmetric hysteresis operator with a cascaded neural network. The hysteresis operator provides a basic hysteresis motion for the FSM. The neural network modifies the basic hysteresis motion to accurately describe the hysteresis nonlinearity of FSMs. The wiping-out and congruency properties of the proposed method are also analyzed. Moreover, the inverse hysteresis model is constructed to reduce the hysteresis nonlinearity of FSMs. The effectiveness of the presented model is validated by experimental results.

scholarly journals Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1362-1370 ◽  
Author(s):  
Yuen Liang ◽  
Suan Xu ◽  
Kaixing Hong ◽  
Guirong Wang ◽  
Tao Zeng
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Piezoelectric Actuators ◽  
Neural Network Modeling ◽  
Proportional Integral Derivative ◽  
Polynomial Fitting ◽  
Proportional Integral ◽  
The Neural Network ◽  
Fitting Method ◽  
One To One

A new polynomial fitting model based on a neural network is presented to characterize the hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural networks can only solve one-to-one mapping, a hysteresis mathematical model is proposed to expand the input of the neural network by converting the multi-valued into one-to-one mapping. Experiments were performed under designed excitation with different driven voltage amplitudes to obtain the parameters of the model using the polynomial fitting method. The simulation results were in good accordance with the measured data and demonstrate the precision with which the model can predict the hysteresis. Based on the proposed model, a single-neuron adaptive proportional–integral–derivative controller combined with a feedforward loop is designed to correct the errors induced by the hysteresis in the piezoelectric actuator. The results demonstrate superior tracking performance, which validates the practicability and effectiveness of the presented approach.

A new hysteresis modeling and optimization for piezoelectric actuators based on asymmetric Prandtl-Ishlinskii model

2020 ◽  
Vol 316 ◽  
pp. 112431
Author(s):  
Wen Wang ◽  
Ruijin Wang ◽  
Zhanfeng Chen ◽  
Zhiqian Sang ◽  
Keqing Lu ◽  
...  
Keyword(s):  
Piezoelectric Actuators ◽  
Modeling And Optimization ◽  
Hysteresis Modeling

scholarly journals Nonlinear Hysteresis Modeling of Piezoelectric Actuators Using a Generalized Bouc–Wen Model

Micromachines ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 183 ◽  
Author(s):  
Jinqiang Gan ◽  
Xianmin Zhang
Keyword(s):  
Least Squares Method ◽  
Compliant Mechanism ◽  
Nonlinear Least Squares ◽  
Piezoelectric Actuators ◽  
Positioning Accuracy ◽  
Rate Dependent ◽  
Nonlinear Hysteresis ◽  
Relaxation Functions ◽  
Hysteresis Modeling ◽  
Nonlinear Least Squares Method

Hysteresis behaviors exist in piezoelectric ceramics actuators (PCAs), which degrade the positioning accuracy badly. The classical Bouc–Wen (CB–W) model is mainly used for describing rate-independent hysteresis behaviors. However, it cannot characterize the rate-dependent hysteresis precisely. In this paper, a generalized Bouc–Wen (GB–W) model with relaxation functions is developed for both rate-independent and rate-dependent hysteresis behaviors of piezoelectric actuators. Meanwhile, the nonlinear least squares method through MATLAB/Simulink is adopted to identify the parameters of hysteresis models. To demonstrate the validity of the developed model, a number of experiments based on a 1-DOF compliant mechanism were conducted to characterize hysteresis behaviors. Comparisons of experiments and simulations show that the developed model can describe rate-dependent and rate-independent hysteresis more accurately than the classical Bouc–Wen model. The results demonstrate that the developed model is effective and useful.

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