Structured moving horizon estimation for linear system chains

Abstract

Computational aspects of moving horizon state estimation are studied for a class of chain networks with bidirectional coupling in the linear state dynamics, and measured outputs. Moving horizon estimation involves solving a quadratic program to minimize the estimation error relative to a model over a fixed window of past input-output observations. By exploiting the spatial structure of a chain, two algorithms for solving this quadratic program are considered. Both algorithms can be distributed in the sense that the computations associated with each sub-system component of the state depend only on information associated with the immediate neighbours. The algorithms differ in the way that the ..