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相关概念视频

Open and closed-loop control systems01:17

Open and closed-loop control systems

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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
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Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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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.

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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.

关键词:
形状记忆合金 (SMA) 是一种形状记忆合金.人工智能控制的人工智能控制非线性歇斯底里斯补偿智能执行器是一个智能执行器.系统性审查 系统性审查

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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.