Complex system decomposition for distributed state estimation based on weighted graph

Abstract

In this work, a systematic subsystem decomposition procedure for complex process systems is proposed for distributed state estimation. In the proposed procedure, both the connectivity within a system and the strength of connections are taken into account. A nonlinear process is first presented by a directed graph in which a node denotes either a state or a measured output of the nonlinear process. In the directed graph, nodes are connected via weighted edges and the weights reflect the strength of connection between two nodes. Based on the weighted directed graph, community structure detection is used to divide all the variables into smaller groups, such that the intra-connection within each group is made much stronger than the interaction among different groups. Subsystem models that are appropriate for distributed state estimation are configured based on the variables assigned to the groups. A comparative study between the proposed procedure with a procedure based on unweighted directed graph is conducted through extensive simulations. In the simulations, a numerical example and a chemical process example are considered. Three commonly used distributed state estimation algorithms designed based on the two decomposition procedures are applied to the two examples. A few guidelines on when to use weighted graphs for decomposition for distributed state estimation are concluded.