TY - JOUR
T1 - A Distributed Optimization Scheme for State Estimation of Nonlinear Networks with Norm-Bounded Uncertainties
AU - Duan, Peihu
AU - Wang, Qishao
AU - Duan, Zhisheng
AU - Chen, Guanrong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - 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.
AB - 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.
KW - Distributed state estimation
KW - regularized least-squares approach
KW - stochastic complex network
KW - uncertainty and nonlinearity
UR - https://www.scopus.com/pages/publications/85112440787
U2 - 10.1109/TAC.2021.3091182
DO - 10.1109/TAC.2021.3091182
M3 - Article
AN - SCOPUS:85112440787
SN - 0018-9286
VL - 67
SP - 2582
EP - 2589
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
IS - 5
ER -