A Distributed Optimization Scheme for State Estimation of Nonlinear Networks With Norm-Bounded Uncertainties

• 成果类型: Article
• 署名作者: Duan, Peihu; Wang, Qishao; Duan, Zhisheng; Chen, Guanrong
• 署名单位: Peking University; Beihang University; City University of Hong Kong
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3091182
• 发表日期: 2022
• 页码: 2582-2589
• 关键词: State estimation; complex networks; uncertainty; Couplings; Time-varying systems; optimization; estimation error; Distributed state estimation; regularized least-squares approach; stochastic complex network; uncertainty and nonlinearity

摘要

This article investigates state estimation for a class of complex networks, in which the dynamics of each node is subject to Gaussian noise, system uncertainties, and nonlinearities. Based on a regularized least-squares approach, the estimation problem is reformulated as an optimization problem, solving for a solution in a distributed way by utilizing a decoupling technique. Then, based on this solution, a class of estimators is designed to handle the system dynamics and constraints. A novel feature of this design lies in the unified modeling of uncertainties and nonlinearities, the decoupling of nodes, and the construction of recursive approximate covariance matrices for the optimization problem. Furthermore, the feasibility of the proposed estimators and the boundedness of mean-square estimation errors are ensured under a developed criterion, which is easier to check than some typical estimation strategies including the linear matrix inequalities-based and the variance-constrained ones. Finally, the effectiveness of the theoretical results is verified by a numerical simulation.