发表机构
Semnan University; Sharif University of Technology; Qom University of Technology; Qatar University; Instituto Superior Tecnico, University of Lisbon; Boston College（塞姆南大学; 谢里夫理工大学; 库姆理工大学; 卡塔尔大学; 里斯本大学高等理工学院; 波士顿学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述系统梳理了网络物理系统中分布式估计、滤波与故障检测算法，涵盖共识、扩散及阈值检测等方法，并探讨了其在智能电网等领域的应用，为研究者提供了全面指南。
AI 中文摘要
本综述概述了网络物理系统（CPS）背景下的分布式估计、滤波和故障检测技术。为建立坚实基础，我们首先定义了线性动力系统的基本方面及相关图论概念，重点强调了对于局部状态估计至关重要的可观测性条件。在讨论共识算法之后，本综述重点介绍了单时间尺度和双时间尺度基于共识的估计与滤波方法。我们提供了这些方法的详细比较分析，考察了它们在资源受限环境中的计算需求、通信开销和性能特征。我们进一步探讨了不同的基于扩散的估计技术和观测冗余设计，以增强对故障和对抗性攻击的韧性和鲁棒性。此外，我们研究了分布式故障检测方法，这些方法能够在大型CPS中实现故障的局部隔离。我们提出了无状态和有状态检测机制，以及平衡检测准确性和误报率的基于阈值的检测技术。这些方法对于维护系统完整性并防止关键基础设施中的级联故障至关重要。本综述最后探讨了多样化的实际应用。我们考察了智能电网和电力网络、社会系统、目标跟踪与定位以及智能交通系统中的实施挑战和算法适应性。通过将理论见解与实际应用相结合，本综述和教程为CPS分布式算法设计领域提供了有价值的理解和研究方向，为新手和经验丰富的研究者提供了应对当前挑战和未来机遇的全面资源。
英文摘要
This survey provides overview of distributed estimation, filtering, and fault detection techniques in the context of cyber-physical systems (CPS). To establish a strong foundation, we first define essential aspects of linear dynamical systems and related graph theoretic concepts, emphasizing observability conditions that are key for local state estimation. After discussing consensus algorithms, this survey highlights single-time and double-time-scale consensus-based estimation and filtering approaches. We provide a detailed comparative analysis of these methodologies, examining their computational requirements, communication overhead, and performance characteristics in resource-constrained environments. We further explore different diffusion-based estimation techniques and observationally redundant designs to enhance resilience and robustness against failures and adversarial attacks. In addition, we investigate distributed fault detection methods that enable local isolation of faults over large-scale CPS. We present both stateless and stateful detection mechanisms, along with threshold-based techniques that balance detection accuracy and false alarm rates. These approaches are essential for maintaining system integrity and preventing cascading failures in critical infrastructure. This survey concludes with an exploration of diverse real-world applications. We examine implementation challenges and algorithm adaptations in smart grid and power networks, social systems, target tracking and localization, and intelligent transportation systems. By bridging theoretical insights with practical applications, this survey and tutorial provides valuable understanding and research directions in the field of distributed algorithm design for CPS, offering both newcomers and experienced researchers a comprehensive resource for addressing current challenges and future opportunities.
CommentsAnnual Reviews in Control
