Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911007285182464 |
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| author | Yuwono, Steve Rana, Muhammad Uzair Schwung, Dorothea Schwung, Andreas |
| author_facet | Yuwono, Steve Rana, Muhammad Uzair Schwung, Dorothea Schwung, Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13164 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory Yuwono, Steve Rana, Muhammad Uzair Schwung, Dorothea Schwung, Andreas Artificial Intelligence Computer Science and Game Theory 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. |
| title | Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory |
| topic | Artificial Intelligence Computer Science and Game Theory |
| url | https://arxiv.org/abs/2506.13164 |