部分电动执行器模型的参数识别
Dongmei Liu1, Jingqu Dong1, Shuai Guo1
1College of Electronic Information Engineering, Changchun University, Changchun 130022, China.
Micromachines
|May 27, 2023
概括
一种新的混合方法结合了粒子群和遗传算法,以确定压电执行器的参数. 这通过建模复杂的非线性歇斯底里来提高定位准确性,实现0.029423μm的低根平均平方误差.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 材料科学 材料科学 材料科学
- 机械电子学是什么意思 机械电子学
背景情况:
- 压电驱动器对于高精度定位系统至关重要.
- 非线性特征,如多值映射和依赖频率的歇斯底里极限执行器精度.
- 准确建模这些非线性对于推进定位技术至关重要.
研究的目的:
- 为非线性压电执行器开发一种有效的参数识别方法.
- 建立一个歇斯底里模型,准确地捕捉这些执行器的复杂行为.
- 提高高精度定位系统的准确性和可靠性.
主要方法:
- 提出了一种混合参数识别方法,该方法结合了粒子群优化 (PSO) 和遗传算法 (GA).
- 整合了PSO的指令性和GA的随机特征,以提高搜索能力.
- 使用开发的混合算法建立了压电执行器的非线性歇斯底里模型.
主要成果:
- 混合方法改善了全球搜索和优化能力,克服了个别PSO和GA的局限性.
- 建立的非线性歇斯底里模型准确地描述了多值映射和频率依赖的歇斯底里.
- 实验验证显示,模型输出与实际执行器输出之间非常一致,根平均平方误差为0.029423μm.
结论:
- 拟议的混合参数识别方法有效地模拟了压电执行器的非线性歇斯底里.
- 这种精确的建模能力提高了高精度定位系统的性能.
- 该研究提供了一种强大的方法,用于解决压电驱动器应用中的非线性.
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