Yang Lei, Ding Bingxiao, Liao Wenhu, Li Yangmin
School of Information Science and Engineering, Jishou University, Jishou 416000, China.
College of Physics and Electromechanical Engineering, Jishou University, Jishou 416000, China.
Micromachines (Basel). 2022 Apr 29;13(5):698. doi: 10.3390/mi13050698.
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.
Preisach模型是一种用于描述磁滞现象的典型标量数学模型，备受关注。然而，Preisach模型的参数识别仍然是一个具有挑战性的问题。本文提出了一种改进的粒子群优化（IPSO）方法来识别Preisach模型参数。首先，通过引入高斯 - 高斯分布函数替代密度函数来建立Preisach模型。其次，采用IPSO算法实现参数识别。最后，将模型参数识别结果与压电致动器的磁滞回线进行比较。与传统粒子群优化（PSO）算法相比，IPSO算法收敛速度更快、计算时间更短且计算精度更高。该方法为压电致动器的Preisach磁滞建模与识别提供了一种有效途径。
