A generalized Prandtl-Ishlinskii model for characterizing the rate-independent and rate-dependent hysteresis of piezoelectric actuators

Gan, Jinqiang; Zhang, Xianmin; Wu, Heng

doi:10.1063/1.4941941

Cited by 49 publications

(23 citation statements)

References 17 publications

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“…The NN inverse model is obtained by the inversion calculation of them. Equations (9) and (10) are the hysteresis inverse model for the main loop, and equations (11) and (12)…”

Section: Combined Feedforward and Feedback Pid Controlmentioning

confidence: 99%

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

Liang

Hong

et al. 2019

Measurement and Control

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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.

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“…The NN inverse model is obtained by the inversion calculation of them. Equations (9) and (10) are the hysteresis inverse model for the main loop, and equations (11) and (12)…”

Section: Combined Feedforward and Feedback Pid Controlmentioning

confidence: 99%

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

Liang

Hong

et al. 2019

Measurement and Control

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“…The physical models have been receiving much research attention for several decades. For example, the Preisach model, 16,17 the Prandtl–Ishlinskii (PI) model, 18 the Duhem model, 19 the Maxwell model, 20 and the Bouc–Wen model. 21,22 A classical Preisach model was developed to describe the hysteresis and the cascaded PD/lead-hysteresis feedback controller was conducted to enhance the positioning accuracy of the system in Song et al.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis modeling of piezoelectric actuator using particle swarm optimization-based neural network

Zhang

Shen

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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Piezoelectric actuators have been received much attention for the advantages of high precision, no wear and rapid response, etc. However, the intrinsic hysteresis behavior of the piezoelectric materials seriously degraded the output performance of piezoelectric actuators. In this paper, to decrease such nonlinear effects and further improve the output performances of piezoelectric actuators, a modified nonlinear autoregressive moving average with exogenous inputs model, which could describe the rate-dependent hysteresis features of piezoelectric actuators was investigated. In the experiment, the different topologies of the proposed back propagation neural network algorithm were compared and the optimal topology was selected considering both the tracking precision and the structure complexity. The experimental results validated that the modified nonlinear autoregressive moving average with exogenous inputs model featured the hysteresis characteristics description ability with high precision, and the predicted motion matched well with the real trajectory. Then, the initial parameters of the back propagation neural network algorithm were further optimized by particle swarm optimization algorithm. The experimental results also verified that the proposed model based on particle swarm optimization–back propagation neural network algorithm was more accurate than that identified through the conventional back propagation neural network algorithm, and has a better predicting performance.

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“…A large amount of literature is devoted to extending the PI model to describe rate-dependent hysteresis. 12–18 However, there are many parameters in these models and the identification process is complicated.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis modeling of piezoelectric micro-positioning stage based on convolutional neural network

Zhong

Yang

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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The inherent hysteresis nonlinearity of piezoelectric actuator degrades the positioning accuracy of the micro-positioning stage. Prandtl–Ishlinskii model is widely used for piezoelectric hysteresis modeling, yet it is a rate-independent model with weak generalization ability. To overcome this problem, we proposed a convolutional neural network model based on the Prandtl–Ishlinskii model, which consists of a rate-dependent Prandtl–Ishlinskii model layer and convolutional network layer. The rate-dependent Prandtl–Ishlinskii model layer extends the traditional Prandtl–Ishlinskii model to describe the rate-dependent hysteresis behavior. The convolutional network layer with deep learning ability extracts the deep features of the input signal to improve the generalization ability of the hysteresis model. The experiment results indicate that the standard error of the proposed hysteresis model to predict displacement at unmodeled frequencies has been reduced by 18.74%–36.75% in comparison with the Prandtl–Ishlinskii model, which verifies that the proposed hysteresis model has not only higher accuracy but also stronger generalization ability.

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A generalized Prandtl-Ishlinskii model for characterizing the rate-independent and rate-dependent hysteresis of piezoelectric actuators

Cited by 49 publications

References 17 publications

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

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

Hysteresis modeling of piezoelectric actuator using particle swarm optimization-based neural network

Hysteresis modeling of piezoelectric micro-positioning stage based on convolutional neural network

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