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A neural-network-based hysteresis model for piezoelectric actuators.

Lianwei Ma1, Yu Shen2, Jinrong Li3

  • 1School of Information Science and Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China.

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Summary

A novel neural network hysteresis model was developed using a variable-order hysteretic operator. This approach effectively models complex hysteresis in piezoelectric actuators, demonstrating high accuracy in experimental validation.

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Area of Science:

  • Engineering
  • Materials Science
  • Control Systems

Background:

  • Hysteresis modeling is crucial for accurate control of systems with nonlinear behavior.
  • Existing models may not fully capture the complex dynamics of hysteresis, particularly in actuators.
  • Piezoelectric actuators exhibit significant hysteresis, impacting their performance and precision.

Purpose of the Study:

  • To introduce a new neural network-based hysteresis model for improved accuracy.
  • To develop a robust hysteresis model applicable to piezoelectric actuators.
  • To validate the proposed model through experimental testing.

Main Methods:

  • A variable-order hysteretic operator (VOHO) was proposed based on motion point trajectory characteristics.
  • A basic hysteresis model (BHM) was constructed using the VOHO.
  • The input space was expanded to two dimensions to facilitate neural network approximation.
  • A neural network was employed to map the expanded input space to the output space.

Main Results:

  • The proposed neural network hysteresis model demonstrated effectiveness in experiments.
  • The model accurately captured the hysteresis behavior of the piezoelectric actuator.
  • Experimental results confirmed the predictive capabilities of the developed model.

Conclusions:

  • The presented neural network-based hysteresis model is effective for systems exhibiting hysteresis.
  • The VOHO and expanded input space approach provide a robust framework for hysteresis modeling.
  • The validated model offers a promising solution for enhancing the control precision of piezoelectric actuators.