Scholarlink

<p>Piezoelectric actuators (PEAs) are widely utilized in precision positioning and high-speed driving due to their exceptional performance. However, their hysteresis characteristics are significantly affected by temperature and driving voltage frequency variations. To address this, the Temperature-Rate Dependent Prandtl-Ishlinskii (TRDPI) model is proposed. This model enhances the Rate Dependent Prandtl-Ishlinskii (RDPI) framework by incorporating dynamic play operators, the Fermi–Dirac distribution function, and a corner compensation term to improve fitting accuracy. Model parameters are identified using an Improved Dung Beetle Optimizer (IDWO) algorithm, which employs Bernoulli chaotic mapping for population initialization, Levy flight strategy and T-distribution for position updates, and fractional-order dynamic boundary adjustment to avoid local optima and improve global search capability. Experiments were conducted to measure the temperature- and frequency-dependent behavior of the PEA under excitation within a frequency range of 1–200&#xa0;Hz, across ambient temperatures ranging from 20 to 60&#xa0;°C. Experimental results demonstrate that the TRDPI model performs better than the RDPI model in fitting the dynamic hysteresis of PEA. The relative maximum and mean square errors reduced to 6.11% and 0.75%, respectively. The IDWO algorithm exhibits rapid convergence and high precision. Furthermore, an inverse TRDPI model-based feedforward controller is proposed, reducing hysteresis to below 3% and significantly improving performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Identification and inverse compensation of temperature-rate dependent hysteresis in piezoelectric actuators

  • 期刊论文

  • 发表日期：

    2026年5月11日

查看全文
  • Yuhang Wu,
  • Lu Liu,
  • Ruina Zhu,
  • Yue Zhang,
  • Yifan Zhou,
  • Shilin Gu,
  • Saijie Mou,
  • Ming Kong

摘要

Piezoelectric actuators (PEAs) are widely utilized in precision positioning and high-speed driving due to their exceptional performance. However, their hysteresis characteristics are significantly affected by temperature and driving voltage frequency variations. To address this, the Temperature-Rate Dependent Prandtl-Ishlinskii (TRDPI) model is proposed. This model enhances the Rate Dependent Prandtl-Ishlinskii (RDPI) framework by incorporating dynamic play operators, the Fermi–Dirac distribution function, and a corner compensation term to improve fitting accuracy. Model parameters are identified using an Improved Dung Beetle Optimizer (IDWO) algorithm, which employs Bernoulli chaotic mapping for population initialization, Levy flight strategy and T-distribution for position updates, and fractional-order dynamic boundary adjustment to avoid local optima and improve global search capability. Experiments were conducted to measure the temperature- and frequency-dependent behavior of the PEA under excitation within a frequency range of 1–200 Hz, across ambient temperatures ranging from 20 to 60 °C. Experimental results demonstrate that the TRDPI model performs better than the RDPI model in fitting the dynamic hysteresis of PEA. The relative maximum and mean square errors reduced to 6.11% and 0.75%, respectively. The IDWO algorithm exhibits rapid convergence and high precision. Furthermore, an inverse TRDPI model-based feedforward controller is proposed, reducing hysteresis to below 3% and significantly improving performance.