Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory
This work addresses the need for more adaptive and efficient temperature control in industrial settings, representing an incremental improvement over existing self-learning approaches.
The paper tackles the problem of enhancing adaptability in PID controllers for industrial systems by introducing an event-based dynamic game theory method, resulting in reduced overshoot and settling time in temperature control loops for printing press machines.
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.