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形状记忆合金执行器的人工智能控制方法:系统性审查和性能分析分析
Stefano Rodinò1, Giuseppe Rota1, Matteo Chiodo1
1Dipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), University of Calabria, 87036 Rende, CS, Italy.
Micromachines
|July 30, 2025
概括
人工智能 (AI) 增强了对形状记忆合金 (SMA) 和磁性SMA (MSMA) 执行器的控制,减轻了非线性和歇斯底里. 人工智能为先进的工程应用提供了更高的精度和适应性.
科学领域:
- *工程和材料科学:专注于先进的执行器技术和控制系统.
- *人工智能:探索AI在复杂的控制方法中的作用.
- * 机器人和机械电子学:针对航空航天,生物医学设备和软机器人的应用.
背景情况:
- * 形状记忆合金 (SMA) 和磁性SMA (MSMA) 执行器提供独特的热力学性能,但由于非线性,歇斯底里和温度灵敏性而存在控制挑战.
- *现有的控制方法难以完全解决这些固有的复杂性,限制了执行器的性能和可靠性.
- * 开发先进的控制策略对于释放SMA和MSMA执行器在苛刻应用中的全部潜力至关重要.
研究的目的:
- *系统地审查和评估基于人工智能 (AI) 的SMA和MSMA执行器的控制方法.
- *分析不同AI控制架构在提高精度,适应性和可靠性的有效性.
- *为特定的执行器类型 (SMA与MSMA) 和应用要求确定最佳的AI策略.
主要方法:
- *以PRISMA为指导的系统文献综述 (2003-2025) 对24项关于人工智能控制SMA和MSMA的研究.
- *控制架构的分类:混合AI-线性,纯AI,自适应和模型预测控制 (MPC).
- *使用根平均平方误差 (RMSE%) 和实验严格度加权评分系统进行定量评估.
主要成果:
- *混合AI线性控制器最常见 (36%);在线训练的神经网络显示出更高的精度 (+2.4%).
- *Feedforward神经网络的表现优于经常性网络 (+3.1%);MPC在SMA方面表现出色 (+5.8%),但在MSMA方面表现不佳 (-7.7%).
- *无传感器策略受益于MSMA系统 (+5.0%),利用电阻进行状态估计.
结论:
- *人工智能有效地减轻SMA和MSMA执行器中的歇斯底里和非线性动力学.
- * 材料特定优化至关重要:SMA倾向于动态控制/MPC;MSMA受益于无传感器AI/纯神经网络.
- *未来的研究应该集中在适应性算法疲劳,嵌入式系统的轻量级AI和标准化的基准测试.
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