Encryption–decryption-based state estimation for nonlinear complex networks subject to coupled perturbation

作者：Peixia Gao, Chaoqing Jia, Aozhan Zhou · 发表于：Systems Science & Control Engineering · 年份：2024 · DOI：10.1080/21642583.2024.2357796 · 被引用次数：60 · 研究领域：Neural Networks Stability and Synchronization、Distributed Control Multi-Agent Systems、Target Tracking and Data Fusion in Sensor Networks

This paper discusses the encryption-decryption-based state estimation (EDBSE) issue for coupled perturbation complex networks (CPCNs) in the framework of the Kalman-type filtering scheme. A uniform distributed random variable is employed to characterize the coupled perturbation among different network units. A uniform-quantization-dependent encryption-decryption (UQDED) scheme is considered here to orchestrate the transmitted data. A novel EDBSE approach is developed such that the upper bounds of prediction error (PE) covariance (PEC) and estimation error (EE) covariance (EEC) can be derived by resolving Riccati-like difference equations and the estimation parameter (EP) is determined by minimizing the trace of the upper bound of EEC. Furthermore, a uniformly bounded condition is elaborated to evaluate the algorithm performance of EDBSE. Finally, an illustrative example is conducted to verify the validity of the introduced EDBSE method.