Queen's University - Utility Bar

QSpace at Queen's University >
Theses, Dissertations & Graduate Projects >
Queen's Theses & Dissertations >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/878

Title: Variance Analysis for Nonlinear Systems
Authors: Yu, Wei

Files in This Item:

File Description SizeFormat
Yu_Wei_200710_PhD.pdf780.15 kBAdobe PDFView/Open
Keywords: Variance ananlysis
variance decomposition
nonlinear control perfromance
sensitvity analysis
Issue Date: Jun-2007
Citation: Journal of Process Control, 17, 607-619
Series/Report no.: Canadian theses
Abstract: In the past decades there has been onsiderable commercial and academic interest in methods for monitoring control system performance for linear systems. Far less has been written on control system performance for nonlinear dynamic / stochastic systems. This thesis presents research results on three control performance monitoring topics for the nonlinear systems: i) Controller assessment of a class of nonlinear systems: The use of autoregressive moving average (ARMA) models to assess the control loop performance for linear systems is well known. Classes of nonlinear dynamic / stochastic systems for which a similar result can be obtained are established for SISO discrete systems. For these systems, the performance lower bounds can be estimated from closed-loop routine operating data using nonlinear autoregressive moving average with exogenous inputs (NARMAX) models. ii) Variance decomposition of nonlinear systems / time series: We develop a variance decomposition approach to quantify the effects of different sources of disturbances on the nonlinear dynamic / stochastic systems. A method, called ANOVA-like decomposition, is employed to achieve this variance decomposition. Modifications of ANOVA-like decomposition are proposed so that the NOVA-like decomposition can be used to deal with the time dependency and the initial condition. iii) Parameter uncertainty effects on the variance decomposition: For the variance decomposition in the second part, the model parameters are assumed to be exactly known. However, parameters of empirical or mechanistic models are uncertain. The uncertainties associated with parameters should be included when the model is used for variance analysis. General solutions of the parameter uncertainty effects on the variance decomposition for the general nonlinear systems are proposed. Analytical solutions of the parameter uncertainty effects on the variance decomposition are provided for models with linear parameters.
Description: Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2007-10-17 16:02:26.376
URI: http://hdl.handle.net/1974/878
Appears in Collections:Queen's Theses & Dissertations
Chemical Engineering Graduate Theses

Items in QSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

  DSpace Software Copyright © 2002-2008  The DSpace Foundation - TOP