使用LSTM神经网络的形状记忆合金执行器模型
Waldemar Rączka1, Marek Sibielak1
1Department of Process Control, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Kraków, Poland.
Materials (Basel, Switzerland)
|July 13, 2024
概括
这项研究使用人工神经网络 (ANN) 建模了形状记忆合金 (SMA) 执行器歇斯底里. ANN有效地捕捉复杂的歇斯底里循环,为控制系统提供了一种新的方法.
科学领域:
- 材料科学 材料科学 材料科学
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 形状记忆合金 (SMA) 对于执行器设计至关重要,但它们固有的非线性和歇斯底里对控制系统构成挑战.
- 现有的hysteresis模型往往缺乏控制理论应用中的实际实用性.
研究的目的:
- 开发一种人工神经网络 (ANN) 模型,用于描述形状记忆合金 (SMA) 执行器的歇斯底里循环.
- 调查ANN在准确描述和建模SMA执行器歇斯底里症方面的能力.
主要方法:
- 利用人工神经网络 (ANN) 模拟SMA执行器的歇斯底里行为.
- 使用Preisach模型生成的训练数据,用于控制和可重复的数据采集.
- 设计并实施了一种特定的ANN结构和培训方法.
主要成果:
- 该研究成功地证明了ANN用于模拟SMA执行器歇斯底里的应用.
- 该ANN方法提供了一种生成受控和可重复数据的方法,尽管没有现实世界的干扰.
- 这项研究探讨了ANN可以代表hysteresis循环的程度.
结论:
- 人工神经网络 (ANN) 为模拟形状记忆合金 (SMA) 执行器所表现的复杂歇斯底里提供了一种可行且有效的方法.
- 这种方法提高了SMA执行器在各种应用中的精确控制潜力.
- 进一步的研究可以探索将干扰因素集成到ANN模型中,以实现更强大的控制.
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