This article presents a novel approach for distributed optimization of production units based on potential game (PG) theory and machine learning. The core of our approach is split into two parts: the first part concentrates on the conceptual treatment of modular installed production units in terms of a PG scenario. The second part focuses on the development and incorporation of suitable learning algorithms to finally form an intelligent autonomous system. In this context, we model the production environment as a state-based PG where each actuator of each module has the role of an agent in the game aiming to maximize its utility value by learning the optimal process behavior. The benefit of the additional state information is visible in the performance of the algorithm making the environment dynamic and serving as a connector between the players. We propose a novel learning algorithm based on a global interpolation method that is applied to a laboratory scale modular bulk good system. The thorough analysis of the encouraging results yields to highly interesting insights into the learning dynamics and the process itself. The benefits of our distributed optimization approach are the plug-and-play functionality, the online capability, fast adaption to changing production requirements, and the possibility of an IEC 61131 conforming to PLC implementation.

中文翻译：

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模块化生产单元的分布式自优化：基于状态的潜在博弈方法

本文提出了一种基于潜在博弈 (PG) 理论和机器学习的生产单元分布式优化的新方法。我们方法的核心分为两部分：第一部分集中在 PG 场景中模块化安装生产单元的概念处理。第二部分侧重于开发和整合合适的学习算法，最终形成一个智能自治系统。在这种情况下，我们将生产环境建模为基于状态的 PG，其中每个模块的每个执行器在游戏中都扮演代理的角色，旨在通过学习最佳过程行为来最大化其效用价值。附加状态信息的好处体现在算法的性能中，使环境动态并充当玩家之间的连接器。我们提出了一种基于全局插值方法的新型学习算法，该算法应用于实验室规模的模块化散装货物系统。对令人鼓舞的结果的彻底分析产生了对学习动态和过程本身非常有趣的见解。我们的分布式优化方法的好处是即插即用功能、在线能力、快速适应不断变化的生产要求以及符合 PLC 实施的 IEC 61131 的可能性。