Abstract
This paper presents a novel method for enhancing the adaptability of Proportional-Integral-Derivative (PID) controllers in industrial systems using event-based dynamic game theory, which enables the PID controllers to self-learn, optimize, and fine-tune themselves. In contrast to conventional self-learning approaches, our proposed framework offers an event-driven control strategy and game-theoretic learning algorithms. The players collaborate with the PID controllers to dynamically adjust their gains in response to set point changes and disturbances. We provide a theoretical analysis showing sound convergence guarantees for the game given suitable stability ranges of the PID controlled loop. We further introduce an automatic boundary detection mechanism, which helps the players to find an optimal initialization of action spaces and significantly reduces the exploration time. The efficacy of this novel methodology is validated through its implementation in the temperature control loop of a printing press machine. Eventually, the outcomes of the proposed intelligent self-tuning PID controllers are highly promising, particularly in terms of reducing overshoot and settling time.
Abstract (translated)
本文提出了一种新颖的方法，利用基于事件的动态博弈理论来增强工业系统中比例-积分-微分（PID）控制器的适应性。这种方法使PID控制器能够自我学习、优化和精细调整。与传统的自学习方法不同，我们提出的框架提供了一种基于事件驱动的控制策略和博弈论学习算法。参与者与PID控制器协作，根据设定点变化和扰动动态调节其增益。 本文还提供了理论分析，证明了在适当的稳定性范围内，给定的游戏具有良好的收敛保证。此外，我们引入了一个自动边界检测机制，帮助玩家找到最优的动作空间初始化，并显著减少探索时间。 该新颖方法的有效性通过将其应用于印刷机温度控制回路的实施得到了验证。最终，提出的智能自适应PID控制器的结果非常有前景，特别是在减少超调和稳定时间方面表现突出。
URL
https://arxiv.org/abs/2506.13164
