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Parameter Identification of Model for Piezoelectric Actuators.

Dongmei Liu1, Jingqu Dong1, Shuai Guo1

  • 1College of Electronic Information Engineering, Changchun University, Changchun 130022, China.

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|May 27, 2023
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Summary

A new hybrid method combines particle swarm and genetic algorithms to identify parameters for piezoelectric actuators. This improves positioning accuracy by modeling complex nonlinear hysteresis, achieving a low root mean square error of 0.029423 μm.

Keywords:
frequency-dependentmulti-valued mappingparticle swarm genetic hybrid methodpiezoelectric actuators

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Area of Science:

  • Robotics and Control Systems
  • Materials Science
  • Mechatronics

Background:

  • Piezoelectric actuators are crucial for high-precision positioning systems.
  • Nonlinear characteristics like multi-valued mapping and frequency-dependent hysteresis limit actuator accuracy.
  • Accurate modeling of these nonlinearities is essential for advancing positioning technology.

Purpose of the Study:

  • To develop an effective parameter identification method for nonlinear piezoelectric actuators.
  • To establish a hysteretic model that accurately captures the complex behavior of these actuators.
  • To enhance the accuracy and reliability of high-precision positioning systems.

Main Methods:

  • A hybrid parameter identification method combining particle swarm optimization (PSO) and genetic algorithms (GA) was proposed.
  • The directivity of PSO and the random characteristics of GA were integrated to improve search capabilities.
  • A nonlinear hysteretic model for piezoelectric actuators was established using the developed hybrid algorithm.

Main Results:

  • The hybrid method improved global search and optimization abilities, overcoming limitations of individual PSO and GA.
  • The established nonlinear hysteretic model accurately described multi-valued mapping and frequency-dependent hysteresis.
  • Experimental validation showed excellent agreement between the model's output and real actuator output, with a root mean square error of 0.029423 μm.

Conclusions:

  • The proposed hybrid parameter identification method effectively models the nonlinear hysteresis of piezoelectric actuators.
  • This accurate modeling capability enhances the performance of high-precision positioning systems.
  • The study provides a robust approach for addressing nonlinearities in piezoelectric actuator applications.