PLC-Informed Distributed Game Theoretic Learning of Energy-Optimal Production Policies
提出一种结合可编程逻辑控制器(PLC)控制代码的博弈论学习方法，通过分布式优化加速生产策略学习，实验表明能降低能耗、提高效率并缩短学习时间。
This article describes a novel concept to optimize manufacturing systems distributively through data-based learning. We propose a game-theoretic (GT) learning set-up that is incorporated with accessible control code of the programmable logic controller (PLC) to accelerate the optimal policies learning procedures, instead of learning everything from scratch. Therefore, we offer to process the accessible and available control code into a GT-based learning framework which is subsequently optimized in a fully distributed manner. To this end, we employ the recently developed framework of state-based potential games (PGs) and prove that under mild conditions PLC-informed (PLCi) learning forms a state-based PG framework. We conduct the experiment on a laboratory scale testbed in numerous production scenarios. The experiment's results highlight the major potential of using the PLCi GT-learning, which is the reduction of energy consumption of the production timescales and improvement of production efficiency while nearly halven the learning times.