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Related Experiment Video

Updated: Jan 18, 2026

Characterization of Full Set Material Constants and Their Temperature Dependence for Piezoelectric Materials Using Resonant Ultrasound Spectroscopy
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Piezoelectric Hysteresis Modeling Under a Variable Frequency Based on a Committee Machine Approach.

Francesco Aggogeri1, Nicola Pellegrini1

  • 1Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.

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|September 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid data-driven model to accurately predict and compensate for hysteresis in piezoelectric actuators. The novel approach significantly reduces modeling errors, improving control system performance.

Keywords:
Bouc–Wenbacklashcommittee machinegenetic algorithmhysteresisparticle swarm optimization

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Area of Science:

  • Mechatronics and Control Systems
  • Nonlinear Dynamics
  • Data-Driven Modeling

Background:

  • Piezoelectric actuators exhibit complex hysteresis, challenging to model with time-varying inputs.
  • Existing dynamic physical models inadequately capture hysteresis in industrial mechatronic devices.
  • Accurate hysteresis modeling is crucial for precise micro-positioning and active control.

Purpose of the Study:

  • To propose a novel hybrid data-driven model for appraising and compensating piezoelectric actuator hysteresis.
  • To integrate Bouc-Wen and backlash hysteresis formulations for enhanced nonlinear effect modeling.
  • To improve the accuracy of piezoelectric actuator models under time-dependent input amplitudes and frequencies.

Main Methods:

  • Developed a hybrid model combining Bouc-Wen and backlash hysteresis formulations.
  • Utilized genetic algorithm (GA) and particle swarm optimization (PSO) for parameter identification.
  • Employed a committee machine approach with frequency clusters for parameter aggregation.
  • Simulated and experimentally validated the piezoelectric actuator performance.

Main Results:

  • Achieved a 23.54% error reduction using the committee machine approach compared to a complete model.
  • The Bouc-Wen sub-model, tuned with GA, yielded a root mean square error (RMSE) of 0.42 µm.
  • Maximum absolute error (MAE) was approximately 0.86 µm within the 150-250 Hz frequency domain.

Conclusions:

  • The proposed hybrid data-driven model effectively appraises and compensates for piezoelectric actuator hysteresis.
  • The committee machine approach offers superior performance in reducing modeling errors.
  • Accurate hysteresis compensation enhances the precision of piezoelectric actuators in control applications.