AI 中文总结
针对部分状态测量下线性同步网络的可观测性阻断问题，提出输出反馈、观测器结合全状态反馈及分布式观测器设计等策略，经数值例子验证方法有效。
AI 中文摘要
大规模网络化系统日益面临可从少量受感染节点推断系统动态的攻击者威胁。本文研究利用有限状态信息阻断此类推断的问题。现有状态反馈方法通过保留特征值实现可观测性阻断，但需要全状态访问，对大型网络不实用。我们提出多种在部分状态测量下运行的控制策略：第一种方法采用输出反馈，在保留部分开环特征值的同时实现可观测性阻断；第二种方法利用观测器重构系统状态，实现全状态反馈控制，保留所有特征值并在传感器布置上提供更大灵活性；我们还将基于观测器的设计扩展到分布式框架。数值例子验证了所提方法的适用范围和有效性。
英文摘要
Large-scale networked systems are increasingly vulnerable to adversaries that can infer system dynamics from a small set of compromised nodes. This paper addresses the problem of blocking such inference using limited state information. While existing state-feedback methods achieve observability blocking with eigenvalue preservation, they require full state access and are impractical for large networks. We propose multiple control strategies that operate under partial state measurements. The first approach employs output feedback to achieve observability blocking while preserving a subset of open-loop eigenvalues. The second approach leverages an observer to reconstruct the system state and enables full-state feedback control, preserving all eigenvalues and providing greater flexibility in sensor placement. We further extend the observer-based design to a distributed framework. Numerical examples demonstrate the scope and validity of the proposed methods.
Comments8 pages, 3 figures, submitted to the IEEE Conference on Decision and Control (CDC)
