Identification of Preisach Model Parameters Based on an Improved Particle Swarm Optimization Method for Piezoelectric Actuators in Micro-Manufacturing Stages

Lei Yang; Bingxiao Ding; Wenhu Liao; Yangmin Li

doi:10.3390/mi13050698

Identification of Preisach Model Parameters Based on an Improved Particle Swarm Optimization Method for Piezoelectric Actuators in Micro-Manufacturing Stages

Micromachines (Basel). 2022 Apr 29;13(5):698. doi: 10.3390/mi13050698.

Authors

Lei Yang¹, Bingxiao Ding^{1

2}, Wenhu Liao^{1

2}, Yangmin Li³

Affiliations

¹ School of Information Science and Engineering, Jishou University, Jishou 416000, China.
² College of Physics and Electromechanical Engineering, Jishou University, Jishou 416000, China.
³ Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.

Abstract

The Preisach model is a typical scalar mathematical model used to describe the hysteresis phenomena, and it attracts considerable attention. However, parameter identification for the Preisach model remains a challenging issue. In this paper, an improved particle swarm optimization (IPSO) method is proposed to identify Preisach model parameters. Firstly, the Preisach model is established by introducing a Gaussian-Gaussian distribution function to replace density function. Secondly, the IPSO algorithm is adopted to Fimplement the parameter identification. Finally, the model parameter identification results are compared with the hysteresis loop of the piezoelectric actuator. Compared with the traditional Particle Swarm Optimization (PSO) algorithm, the IPSO algorithm demonstrates faster convergence, less calculation time and higher calculation accuracy. This proposed method provides an efficient approach to model and identify the Preisach hysteresis of piezoelectric actuators.

Keywords: Preisach hysteresis; improved particle swarm optimization; piezoelectric materials.

Grants and funding

This work is supported by the Huxiang High-Level Talent Project of Hunan Province (Grant No. 2019RS1066), the Project State Key Laboratory of Ultra-Precision Machining Technology of Hong Kong Polytechnic University (Project ID. P0033453) and the Scientific Research Project of Jishou University (Project ID. jdy21068).