论文标题
一种分布式优化方案，用于具有规范不确定性的非线性网络的状态估计
A Distributed Optimization Scheme for State Estimation of Nonlinear Networks with Norm-bounded Uncertainties
论文作者
论文摘要
本文研究了一类复杂网络的状态估计问题，其中每个节点的动力学均受高斯噪声，系统不确定性和非线性的约束。基于一种正规化的最小二乘方法，估计问题被重新制定为一个优化问题，通过利用退耦技术以分布式方式解决解决方案。然后，基于此解决方案，一类估计器旨在处理系统的动态和约束。这种设计的新功能在于不确定性和非线性的统一建模，节点的解耦以及用于优化问题的递归近似值协方差矩阵的构建。此外，在开发的标准下，确保提出的估计器的可行性和均方误差的界限，这比包括基于线性矩阵不平等的某些典型估计策略更容易检查，以及差异约束。最后，通过数值模拟验证了理论结果的有效性。
This paper investigates the state estimation problem 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 the mean-square 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.
