A nonlinear predictive control of processes with multiscale objectives using a fuzzy-system identification approach

Ali Rahnamoun, Antonios Armaou

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

3 Scopus citations

Abstract

In this paper the problem of model based control of a microscopic process is investigated. The unavailability of closed-form models as well as the ill-definition of variables to describe the process evolution makes the controller design task challenging. We address this problem via a fuzzy system identification of the dominant process dynamics. The data required for the system identification of such processes is produced employing atomistic simulations. A methodology is developed in which fuzzy logic for nonlinear system identification is coupled with nonlinear model predictive Control for control of microscopic processes. We illustrate the applicability of the proposed methodology on a Kinetic Monte Carlo (KMC) realization of a simplified surface reaction scheme that describes the dynamics of CO oxidation by O 2 on a Pt catalytic surface. The nonlinear fuzzy model gives a good approximation to the system even without using filter for the system and the proposed controller successfully forces the process from one stationary state to another state.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Pages4976-4981
Number of pages6
StatePublished - Sep 29 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Publication series

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

Other

Other2011 American Control Conference, ACC 2011
CountryUnited States
CitySan Francisco, CA
Period6/29/117/1/11

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Rahnamoun, A., & Armaou, A. (2011). A nonlinear predictive control of processes with multiscale objectives using a fuzzy-system identification approach. In Proceedings of the 2011 American Control Conference, ACC 2011 (pp. 4976-4981). [5991433] (Proceedings of the American Control Conference).