Partition-based distributed moving horizon state estimation with system disturbances and sensor noise penalties

Abstract

In this article, partition-based distributed state estimation of general linear systems is considered. A distributed moving horizon state estimation algorithm is developed via partitioning the entire system model and the global objective function of centralized moving horizon estimation into subsystem models and local objective functions, respectively. Based on moving horizon estimation, we design unconstrained subsystem estimators of the distributed scheme. These estimators are required to be executed iteratively within each sampling period. The objective function of each estimator penalizes both the estimates of system disturbances and the estimate of output measurement noise. Convergence and stability of the estimation error dynamics are analyzed under the unconstrained setting. A benchmark chemical example is used to illustrate the proposed approach.