非线性系统的一种分布式状态估计算法及其稳定性分析

Distributed state estimation algorithm for nonlinear systems and its stability analysis

针对连续时变非线性系统的状态估计,提出了一种基于自适应高增益扩展卡尔曼滤波的分布式估计方法。首先考虑一类非线性系统,由通过状态彼此相互作用的几个子系统构成;然后针对每个子系统设计一个自适应高增益扩展卡尔曼滤波器,一个子系统的滤波器基于子系统的输出测量值和与其他滤波器交换的信息来估计子系统的状态;不同子系统的自适应高增益扩展卡尔曼滤波器相互通信来交换信息,以获得整个系统的状态估计值;最后对所提出的分布式状态估计的稳定性进行了分析。通过对一个化工过程实例的应用,表明了所提出的分布式状态估计设计的有效性和适用性。

Aiming at state estimation of continuous time-varying nonlinear systems,a distributed estimation method based on adaptive high-gain extended Kalman filtering is proposed in this paper.Firstly,a class of nonlinear systems is considered,which are composed of several subsystems interacting with each other via their states.Then an adaptive high-gain extended Kalman filter is designed for each subsystem.The filter of a subsystem estimates the subsystem state based on the subsystem output measurements and information exchanged with other filters.The adaptive high-gain extended Kalman filter of different subsystems communicates with each other to exchange information to obtain the state estimation of the entire system.Finally,the stability of the proposed distributed state estimation is analyzed.The application to a chemical process example shows the effectiveness and applicability of the proposed distributed state estimation design.