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Distributional uncertainty analysis using polynomial chaos expansions
conference contribution
posted on 2011-01-13, 16:14 authored by Zoltan NagyZoltan Nagy, Richard D. BraatzAbstract—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
© IEEEVersion
- VoR (Version of Record)
Publication date
2010Notes
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
9781424453542Publisher version
Language
- en