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Distributional uncertainty analysis using polynomial chaos expansions

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conference contribution
posted on 13.01.2011, 16:14 authored by Zoltan NagyZoltan Nagy, Richard D. Braatz
Abstract—A computationally efficient approach is presented that quantifies the influence of parameter uncertainties on the states and outputs of finite-time control trajectories for nonlinear systems, based on the approximate representation of the model via polynomial chaos expansion. The approach is suitable for studying the uncertainty propagation in open-loop or closed-loop systems. A quantitative and qualitative assessment of the method is performed in comparison to the Monte Carlo simulation technique that uses the nonlinear model for uncertainty propagation. The polynomial chaos expansion-based approach is characterized by a significantly lower computational burden compared to Monte Carlo approaches, while providing a good approximation of the shape of the uncertainty distribution of the process outputs. The techniques are applied to the crystallization of an inorganic chemical with uncertainties in the nucleation and growth parameters.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Citation

NAGY, Z.K. and BRAATZ, R.D., 2010. Distributional uncertainty analysis using polynomial chaos expansions. IN: IEEE International Symposium on Computer-Aided Control System Design (CACSD), Yokohama, 8-10 Sept, 7pp.

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2010

Notes

This is a conference paper [©IEEE]. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

9781424453542

Language

en

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