将基于模型的深度学习与基于模块状态的Stackelberg游戏集成为自优化分布式生产系统
IEEE transactions on cybernetics
|September 25, 2025
概括
这项研究将深度学习与基于模块状态的Stackelberg游戏 (Mod-SbSG) 集成在一起,以在制造业中实现高效的自我优化. 这种新的方法通过学习动态和虚拟培训显著减少了昂贵的真实世界系统交互.
科学领域:
- 制造系统工程 制造系统工程
- 人工智能的人工智能
- 控制理论 控制理论
背景情况:
- 没有模型的模块化基于状态的Stackelberg游戏 (Mod-SbSG) 需要广泛的真实世界的交互来进行优化.
- 在工业环境中,由于成本,时间和安全问题,这些互动往往是不切实际的.
- 之前对数字双胞胎的依赖,用于训练Mod-SbSG玩家,但由于难以创建准确的系统表示而受到限制.
研究的目的:
- 引入一种新的框架,将深度学习与Mod-SbSG相结合,用于制造业的分布式自优化.
- 提高采样效率,减少对现实世界系统交互的依赖.
- 开发一种学习系统动态的方法,使用深度学习来优化政策.
主要方法:
- 基于深度模型的学习与基于模块状态的斯塔克尔伯格游戏 (Mod-SbSG) 的集成.
- 开发深度学习模型,准确预测系统动态.
- 在优化的虚拟环境中训练Mod-SbSG玩家,利用学习的动态.
主要成果:
- 拟议的框架成功地用深度学习取代了对准确数字表示的需求.
- 单步和多步预测器对系统动态的证明有效性.
- 通过网络重用和转移学习,在实验室测试场景中实现了实体系统交互的77.78%的减少.
结论:
- 集成深度学习的Mod-SbSG框架为制造业的分布式自优化提供了一个样本效率高且实用的方法.
- 这种方法显著降低了与物理系统直接交互的需要,克服了传统方法的局限性.
- 该框架显示了可适应的工业控制系统的前景,提高了效率并降低了运营风险.
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