Fuzzy-System-Based Multi-Agent Consensus Control
Abstract: In recent years, multi-agent systems (MAS) have garnered significant attention across various fields, including automation, robotics, distributed computing, and networked systems. These systems comprise multiple autonomous agents that operate collaboratively to achieve common objectives, often without centralized control. A key challenge in such systems is the consensus problem, where all agents must converge to a common state or value despite their initial differences and the presence of uncertainties.
Traditional consensus control methods typically rely on precise mathematical models and assume a deterministic environment, where the system dynamics and interactions between agents are well-defined and predictable. However, real-world applications are fraught with uncertainties, including modeling inaccuracies, environmental disturbances, communication delays, and noise. These uncertainties can significantly degrade the performance of traditional consensus algorithms, leading to slow convergence or even instability.
To address these limitations, this paper introduces a novel fuzzy-system-based consensus control approach for multi-agent systems. Fuzzy logic, known for its ability to handle imprecision and uncertainty, is employed to design a control strategy that is both robust and adaptable. The proposed approach leverages fuzzy logic to manage the inherent uncertainties in multi-agent systems, ensuring that agents reach consensus even in the presence of significant disturbances and variations in system parameters.
The main contributions of this paper are threefold. First, a fuzzy-system-based control method is developed, providing a more flexible and robust alternative to traditional consensus algorithms. Second, a detailed design of a fuzzy controller is presented, demonstrating how fuzzy logic can be effectively integrated into the consensus control framework. Third, the proposed method is validated through extensive simulations, which show that it outperforms conventional methods in terms of robustness, convergence speed, and adaptability to changing conditions.
The results of this study suggest that fuzzy logic offers a powerful tool for enhancing the performance of consensus control in multi-agent systems, making them more resilient to uncertainties and better suited for real-world applications.
Submission Number: 239
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