强化基于学习的控制和网络共设计,用于工业物联网
Hansong Xu1, Xing Liu1, Wei Yu1
1Towson University, USA.
IEEE journal on selected areas in communications : a publication of the IEEE Communications Society
|August 9, 2023
概括
本研究介绍了强化学习,用于自动配置工业物联网 (IIoT) 系统. 该方法在动态的工业环境中快速适应控制和网络,增强自动化.
科学领域:
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
- 自动化 自动化 自动化
背景情况:
- 工业物联网 (IIoT) 或工业4.0将物联网 (IoT) 整合到制造业中,以提高连接性和效率.
- 在IIoT中的网络物理系统 (CPS) 涉及控制,网络和计算系统的交互合成.
- 动态的工业运作给IIoT系统设计和自动化带来了挑战,原因是复杂的系统相互作用.
研究的目的:
- 在动态的工业环境中利用强化学习 (RL) 来自动配置控制和网络系统.
- 制定针对工业系统特征而定制的新型RL政策,以确保快速融合.
- 通过在无线网络物理系统上进行广泛的模拟来验证基于RL的联合设计方法.
主要方法:
- 利用强化学习技术进行自适应系统配置.
- 设计了三个针对工业系统动态和快速趋同的优化RL新政策.
- 在一个现实的无线网络物理模拟器上实现并测试了基于RL的共同设计.
主要成果:
- 基于RL的方法证明了控制和网络系统的有效和快速自动重新配置.
- 实验结果证实了该方法能够处理动态的工业环境.
- 设计的政策促进了RL算法的更快的融合.
结论:
- 提出的基于强化学习的共同设计方法成功地在动态的工业环境中自动化了IIoT系统配置.
- 该方法为提高工业4.0的自动化和适应性提供了一个有希望的解决方案.
- 该研究验证了针对复杂的网络物理系统优化量身定制的RL政策的有效性.
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