Fast Moving Horizon Estimation of nonlinear processes via Carleman linearization

Negar Hashemian, Antonios Armaou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations


Moving Horizon Estimation (MHE) is a general method employed in many dynamic systems to monitor unmeasurable states. MHE can handle unavoidable physical constraints on the system by a constrained nonlinear optimization problem. However, since this approach requires repeated solving of the optimization problem, it is usually limited to slow-evolving, quasi-linear, low-order systems. In this work, we propose a method that accelerates the optimization procedure. To achieve this goal, Carleman linearization technique is employed to obtain a linear representation of a generic nonlinear system. Then, the sensitivity of the estimation error, gradient vector and Hessian matrix of the objective function are analytically derived. This information about the objective function significantly reduces computational costs and errors associated with numerical approximations of derivatives. Even though the representation appears linear, it is in fact a higher order approximation. Simulation results for a crystallization process show the efficiency and performance of the designed observer.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781479986842
StatePublished - Jul 28 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2015 American Control Conference, ACC 2015
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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