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http://dx.doi.org/10.5302/J.ICROS.2014.14.9015

A Survey on State Estimation of Nonlinear Systems  

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)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.20, no.3, 2014 , pp. 277-288 More about this Journal
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.
Keywords
review; state estimation method; nonlinear system; optimization approach; bayesian approach;
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