Approximating arrival costs in distributed moving horizon estimation: A recursive method

TL;DR

This paper introduces a recursive approach to approximate arrival costs in distributed moving horizon estimation, enhancing stability and applicability to nonlinear constrained systems.

Contribution

It develops a recursive arrival cost approximation method for distributed moving horizon estimation, extending linear system results to nonlinear systems with stability guarantees.

Findings

The method improves estimation accuracy in distributed nonlinear systems.

The approach ensures stability under certain conditions.

Benchmark results demonstrate superior performance over existing methods.

Abstract

In this paper, we present a new approach to distributed moving horizon estimation for constrained nonlinear processes. The method involves approximating the arrival costs of local estimators through a recursive framework. First, distributed full-information estimation for linear unconstrained systems is presented, which serves as the foundation for deriving the analytical expression of the arrival costs for the local estimators. Subsequently, we develop a recursive arrival cost design for linear distributed moving horizon estimation. Sufficient conditions are derived to ensure the stability of the estimation error for constrained linear systems. Next, we extend the arrival cost design derived for linear systems to account for nonlinear systems, and a partition-based constrained distributed moving horizon estimation algorithm for nonlinear systems is formulated. A benchmark chemical process is used to illustrate the effectiveness and superiority of the proposed method.