使用FAE-CAE-LSTM和深度增强学习进行非线性动态系统的传感器驱动替代建模和控制
Mahdi Kherad1, Mohammad Kazem Moayyedi2, Faranak Fotouhi-Ghazvini1
1Department of Computer Engineering and IT, University of Qom, Qom 46611, Iran.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一个新的框架,用于控制使用深度强化学习 (DRL) 的复杂系统. 通过FAE-CAE-LSTM方法,可实现非线性动态系统的高效实时传感器控制.
科学领域:
- 网络物理系统
- 非线性动力学
- 计算流体动力学
背景情况:
- 非线性偏微分方程 (PDEs) 的实时控制由于数据稀疏和高维度而具有挑战性.
- 深度强化学习 (DRL) 显示出潜力,但需要在全序模拟上进行密集的计算训练.
研究的目的:
- 为非线性动态系统基于DRL的控制开发一个计算效率高的传感器驱动框架.
- 通过非侵入性减少顺序建模 (NIROM) 实现实时控制.
主要方法:
- 引入了FAE-CAE-LSTM:一个结合自动编码器和LSTM的框架,用于状态压缩和时间演变.
- 通过类似传感器的时空测量,在减少的潜伏空间中训练DRL代理.
- 使用CNN-MLP奖励估计器进行数据驱动的反,而不需要管理方程.
主要成果:
- 证明了准确的状态重建和对基准系统的强有力的控制 (例如,汉堡方程,过去的气流量).
- 与传统的基于模拟的培训相比,实现了显著的计算加速.
- 验证了FAE-CAE-LSTM替代品对可扩展的DRL控制的有效性.
结论:
- FAE-CAE-LSTM框架有效地实现了基于传感器的实时和可扩展的DRL控制.
- 这种方法克服了网络物理系统中稀疏数据和高维度的局限性.
- 该方法为控制复杂的非线性动态系统提供了显著的进步.
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