Distributed simultaneous state and parameter estimation of nonlinear systems

TL;DR

This paper introduces a distributed estimation method for nonlinear systems with partially observable states and parameters, utilizing sensitivity analysis and community structure detection to improve estimation accuracy.

Contribution

The paper presents a novel distributed moving horizon estimation approach combining sensitivity analysis and community structure detection for nonlinear systems.

Findings

Effective estimation in a chemical process with interconnected reactors

Improved accuracy through subsystem communication

Validation demonstrates robustness of the proposed method

Abstract

In this paper, we consider distributed simultaneous state and parameter estimation for a class of nonlinear systems, for which the augmented model comprising both the states and the parameters is only partially observable. Specifically, we first illustrate how the sensitivity analysis (SA) can select variables for simultaneous state and parameter estimation. Then, a community structure detection (CSD) based process decomposition method is proposed for dividing the entire system into interconnected subsystems as the basis of distributed estimation. Next, we develop local moving horizon estimators based on the configured subsystem models, and the local estimators communicate with each other to exchange their estimates. Finally, an SA and CSD based distributed moving horizon estimation (DMHE) mechanism is proposed. The effectiveness of the proposed approach is illustrated using a chemical process consisting of four connected reactors.