In this paper, the joint state and fault estimation problem is investigated for a class of discrete-time complex networks with measurement saturations and stochastic nonlinearities. The difference between the actual measurement and the saturated measurement is regarded as an unknown input and the system is thus re-organized as a singular system. An appropriate estimator is designed for each node which aims to estimate the system states and the loss of the actuator effectiveness simultaneously. In the presence of measurement saturations and stochastic nonlinearities, upper bounds of the error covariances of the fault estimates are recursively obtained and then minimized. Sufficient conditions are proposed to guarantee the existence, unbiasedness, and boundeness of the developed estimator. Our developed estimator design algorithm is distributed because it depends only on the local information and the information from the neighboring nodes, thereby avoiding the usage of a center estimator. Finally, simulation results are presented to show the performance of the proposed strategy in simultaneously estimating the states and faults.

中文翻译：

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测量饱和和随机非线性下复杂网络的联合状态和故障估计


本文研究了一类具有测量饱和度和随机非线性的离散时间复杂网络的联合状态和故障估计问题。实际测量值和饱和测量值之间的差异被视为未知输入，因此系统被重新组织为奇异系统。为每个节点设计了适当的估计器，旨在同时估计系统状态和执行器有效性的损失。在存在测量饱和度和随机非线性的情况下，递归地获得故障估计的误差协方差的上限，然后将其最小化。提出了充分条件来保证所开发的估计量的存在性、无偏性和有界性。我们开发的估计器设计算法是分布式的，因为它仅依赖于本地信息和来自相邻节点的信息，从而避免了中心估计器的使用。最后，仿真结果显示了所提出的策略在同时估计状态和故障方面的性能。