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

2025-06-16

AI Summary

In industrial temperature control systems, conventional PID controllers suffer from excessive overshoot, slow settling, and poor adaptability under setpoint step changes and external disturbances. To address these limitations, this paper proposes an event-triggered, dynamic-game-driven real-time self-tuning PID method. The approach introduces a novel event-driven multi-agent game-theoretic learning framework, incorporating automatic boundary detection to accelerate action-space initialization, and provides a rigorous proof of closed-loop system convergence. By synergistically integrating event-triggered control, online reinforcement learning, and dynamic PID gain optimization, the method achieves significant performance improvements: in a printing press temperature loop experiment, overshoot is reduced by 42% and settling time shortened by 37%. Experimental results validate the method’s strong robustness and millisecond-level real-time adaptive capability.

Technology Category

• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Behavior Learning & Control

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Enhancing PID controller adaptability using game theory
• Dynamic adjustment of PID gains for set point changes
• Reducing overshoot and settling time in temperature control

Innovation

• Event-based game theory for PID tuning
• Dynamic gain adjustment via collaboration
• Automatic boundary detection mechanism