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A Survey on State Estimation of Nonlinear Systems

비선형 시스템의 상태변수 추정기법 동향

  • Jang, Hong (Chemical & Biomolecular Engineering, Korea Advanced Institute of Science and Technology) ;
  • Choi, Su-Hang (Chemical & Biomolecular Engineering, Korea Advanced Institute of Science and Technology) ;
  • Lee, Jay Hyung (Chemical & Biomolecular Engineering, Korea Advanced Institute of Science and Technology)
  • 장홍 (한국과학기술원 생명화학공학과) ;
  • 최수항 (한국과학기술원 생명화학공학과) ;
  • 이재형 (한국과학기술원 생명화학공학과)
  • Received : 2014.01.24
  • Accepted : 2014.02.03
  • Published : 2014.03.01

Abstract

This article reviews various state estimation methods for nonlinear systems, particularly with a perspective of a process control engineer. Nonlinear state estimation methods can be classified into the following two categories: stochastic approaches and deterministic approaches. The current review compares the Bayesian approach, which is mainly a stochastic approach, and the MHE (Moving Horizon Estimation) approach, which is mainly a deterministic approach. Though both methods are reviewed, emphasis is given to the latter as it is particularly well-suited to highly nonlinear systems with slow sampling rates, which are common in chemical process applications. Recent developments in underlying theories and supporting numerical algorithms for MHE are reviewed. Thanks to these developments, applications to large-scale and complex chemical processes are beginning to show up but they are still limited at this point owing to the high numerical complexity of the method.

Acknowledgement

Supported by : 한국연구재단

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