摘要
针对压电执行器的迟滞非线性问题,提出一种压电执行器迟滞非线性建模方法,通过引入迟滞非线性项和反余切函数对经典Duhem模型进行改进,再根据实验测量的压电执行器位移数据,使用引入正弦余弦学习因子的改进粒子群算法对迟滞模型进行参数辨识。结果表明,改进粒子群算法参数拟合的适应度均优于传统粒子群算法。此外,相比经典Duhem模型,改进Duhem模型能够更加精确地描述压电执行器在高振幅高激励下的迟滞现象,具有较好的激励泛化性。通过对10组实验结果进行误差分析,改进Duhem模型的均方根误差平均下降了89.47%,验证了所提方法的有效性。
As an important component in the field of precision measurement, piezoelectric actuators boasts huge market potentials. The hysteresis nonlinearity of piezoelectric actuators is also a widely researched topic in the field of precision displacement control and piezoelectric driving technology. Currently, the modeling research on the hysteresis nonlinearity of piezoelectric actuators has been relatively well developed, but there are still some limitations. First, the physical model of hysteresis nonlinearity in piezoelectric actuators involves complex physical processes and exhibits uncertainties when applied to practical systems. Second, although the phenomenological models of hysteresis nonlinearity describe the hysteresis characteristics of piezoelectric actuators directly using input-output mapping relationships, which are more applicable, few of these models can simultaneously describe the asymmetry, rate dependency, and excitation generalization of hysteresis nonlinearity.To address the low prediction accuracy and complexity of existing rate-dependent hysteresis models, this paper proposes a new modeling method for hysteresis nonlinearity in piezoelectric actuators. Based on the classical Duhem hysteresis model, hysteresis factors and inverse tangent functions are introduced to characterize the hysteresis behavior of piezoelectric actuators. To address the issue of low prediction accuracy, this paper analyzes the parameter identification characteristics of the Particle Swarm Optimization algorithm and proposes an improved Particle Swarm Optimization algorithm based on sine and cosine learning factors. This algorithm ensures population diversity while improving the global search capability, achieving precise identification of model parameters. To comprehensively evaluate the performance of the improved model and parameter identification method and ensure their effectiveness, experiments are conducted using ten sets of input signals with large amplitude and frequency spans, including sine, triangular, and mixed-frequency signals. The modeling errors of the proposed model are analyzed in detail.The experimental results show the improved Particle Swarm Optimization algorithm effectively avoids the problem of getting trapped in local optima. The improved Duhem model accurately describes the rate-dependent hysteresis characteristics of piezoelectric actuators under high-frequency and high-amplitude excitations, and it exhibits good excitation generalization. This provides a new model choice and parameter identification method for the characterization of piezoelectric hysteresis nonlinearity and model parameter identification.
作者
王宇航
白雨鑫
郭金堤
国珈珲
张健
张韬
WANG Yuhang;BAI Yuxin;GUO Jindi;GUO Jiahui;ZHANG Jian;ZHANG Tao(School of Mechatronic Engineering,Northeast Forestry University,Harbin 150000,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2023年第12期112-121,共10页
Journal of Chongqing University of Technology：Natural Science
基金
国家重点研究发展计划项目(2019YFB2004900)
黑龙江省重点研发项目(GZ20220105)
中央高校基础研究基金项目(2572023CT14-05)。
关键词
压电执行器
迟滞非线性
Duhem模型
粒子群算法
piezoelectric actuator
hysteresis nonlinearity
Duhem model
particle swarm optimization
