线性同构多智能体系统一致性协议研究综述

A review of consensus protocols for linear homogeneous multi-agent systems

摘要:

为系统性地梳理与剖析线性同构多智能体系统一致性问题的研究现状、核心挑战与未来发展趋势,为相关领域的研究人员提供清晰的理论图谱与前沿指引,文中对该领域内的一致性协议进行了全面、多维度的综述与展望。首先,从奠定该领域理论基石的几类经典线性时不变协议切入,深入剖析了其将单智能体观测-控制理论推广至网络化环境的设计哲学,并提炼出一个融合多种基础协议的通用分析框架。其次,以此为基点,遵循从基础理论到前沿应用的逻辑脉络,对为应对复杂现实约束而衍生出的各类高级协议进行深入探讨。这些协议包括:旨在消除对全局信息依赖的完全分布式协议、消除控制器间信息交换的攻击免疫协议；用于克服通信瓶颈的时延补偿协议；可实现更快收敛速度和更强鲁棒性的有限时间/预定时间一致性协议,以及旨在确保对抗性环境下系统可靠性的抗攻击与容错协议。最后,总结了当前一致性协议的研究成果与未来发展所面临的挑战,并展望了下一阶段的探索方向。结果表明,线性多智能体系统的一致性研究已形成较为完整的理论体系。然而,现有研究仍存在局限性,例如,针对多种挑战并存的复合型问题的统一理论框架尚不成熟,传统的基于模型的控制方法在应对高动态、强不确定性环境时适应性不足。结论认为,未来一致性研究的核心突破方向在于:构建能够融合处理复合型挑战的统一理论框架、深化数据驱动与学习型控制的理论与应用,以及推动研究范式从状态层面的“一致”向任务层面的“协同”演进。

Abstract:

To systematically analyze the state-of-the-art, core challenges, and future trends of the consensus problem for linear homogeneous multi-agent systems, this paper presents a comprehensive, multi-dimensional review of consensus protocols, offering a clear theoretical landscape and forward-looking guidance for researchers in the field. The review begins with foundational classic linear time-invariant protocols, analyzing their design philosophy of extending single-agent observer-control theory to networked environments and proposing a unified analytical framework. Building on this, the paper investigates advanced protocols developed to address complex real-world constraints. These include: fully distributed protocols designed to eliminate reliance on global information; attack-immune protocol that eliminates information exchange between controllers; time-delay compensation protocols for overcoming communication bottlenecks; finite-time/prescribed-time consensus protocols for achieving faster convergence and stronger robustness; and attack-resilient and fault-tolerant protocols to ensure reliability in adversarial environments. Finally, current research achievements in consensus protocols and the challenges regarding their future development are summarized, followed by an outlook on potential directions for the next stage of exploration. The findings indicate that research on consensus for linear multi-agent systems has established a relatively complete theoretical framework. However, limitations persist: a unified theory for addressing coexisting, compound challenges remains underdeveloped, and traditional model-based control methods lack adaptability in highly dynamic and uncertain environments. In conclusion, key future breakthroughs are expected in: developing unified frameworks to handle compound challenges, advancing the theory and application of data-driven and learning-based control, and shifting the research paradigm from state-level “consensus” to task-level “coordination”.