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Automated leaders and followers selection in Stackelberg games for modular manufacturing systems
(2025)
We propose an automated data-driven mechanism to evaluate each actuator’s influence on modular manufacturing system objectives. This mechanism enables automated leader and follower selection in Stackelberg games, which have gained traction in engineering. While leaders typically hold greater priority or influence, systematic methods for assigning leaders and followers remain limited, especially in large-scale applications. As a result, many rely on empirical analysis to assign leaders and followers, without confirming if the selected setup is optimal. Therefore, in this paper, we develop an automated mechanism to evaluate the influence of each player on the global objectives of modular manufacturing systems, applicable to both single- and multi-objective scenarios. We validate the proposed approach within an industrial control scenario, using a modular Bulk Good System and its larger-scale arrangement with sequential and serial-parallel processes. We focus on three objectives, including production flow disruption prevention, power consumption reduction, and production demand fulfilment. Finally, we implement a self-learning algorithm utilizing Modular State-based Stackelberg Games, where leaders and followers are selected using our approach, which leads to encouraging results.
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.
Purpose – The complexity of developing product service systems continues to increase. At the same time, development cycles are becoming ever shorter and the requirements for safety, conformity and sustainability ever higher. In this article, a meta-analysis is carried out on the possibility of integrating sustainability aspects as requirements in a systems engineering approach and the differences are discussed.
Methodology/approach - A systematic literature search of articles in ScienceDirect between 2016 and 2023 is carried out. The keywords are sustainability and system engineering incl. synonyms. In a further step, the papers are screened for their relevance to product service systems and the relevance for the purpose.
Findings – To address sustainability in complex technical systems, a holistic, model-based approach is required to ensure that sustainability aspects are considered. By using frameworks and methods such as macPro² and SysLM, requirements and indicators for sustainability can be integrated into the development phase, whereas currently only the environmental dimension is usually considered via resource and energy indicators.
Research limitations/implications – In order to achieve a complete picture, additional databases must be considered and research results from ongoing projects must be awaited.
Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg games
(2025)
In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders’ decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5%–13% while satisfying the production demand, which significantly improves potential (global objective) values.