Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory

TL;DR

This paper introduces an event-based game theory approach for self-tuning PID controllers, enabling real-time adaptation and optimization in temperature control systems with proven convergence and improved performance.

Contribution

It presents a novel event-driven, game-theoretic framework for self-learning PID controllers with convergence guarantees and automatic boundary detection for faster tuning.

Findings

Reduced overshoot in temperature control

Faster settling times achieved

Effective self-tuning demonstrated in industrial setting

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.