Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/xmlui/handle/10054/3871
Title: From data to nonlinear predictive control. 1. Identification of multivariable nonlinear state observers
Authors: SRINIVASARAO, M
PATWARDHAN, SC
GUDI, RD
Keywords: Orthonormal Basis Filters
System-Identification
Models
Diagnosis
Bases
Issue Date: 2006
Publisher: AMER CHEMICAL SOC
Citation: INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 45(6), 1989-2001
Abstract: This work aims at the identification of a special class nonlinear state space observers for nonlinear multivariable systems directly from input-output data when the data is corrupted with unmeasured disturbances. At the identification stage, the one step ahead predictor form of the model is arranged to have a Weiner-like structure. The linear dynamic component of the predictor is parametrized using generalized orthonormal basis functions. The resulting observer is shown to be a nonlinear ARX (NARX) type model with an infinite but fading memory property. It is also shown that the proposed model structure is capable of capturing input as well as output multiplicity (multiple steady states) behavior. The efficacy of the proposed modeling scheme is demonstrated using simulation studies on a continuously stirred tank reactor (CSTR) process model, which exhibits input multiplicity, and another CSTR process model that exhibits output multiplicities. The types of unmeasured disturbances investigated are (a) Unknown input disturbances (such as feed concentration fluctuations), (b) uncertainties in manipulated inputs, and (C) fluctuation in process parameters. The proposed modeling scheme is also validated in real time using a laboratory scale, multivariable experimental system. The analysis of the simulation and experimental studies reveals that the identified models have excellent disturbance modeling and long range prediction abilities. The identified models are also able to capture the steady-state behavior of the systems under consideration reasonably accurately over a wide operating range. The resulting stochastic model can be directly used for the development of an extended Kalman filter and to formulate a nonlinear model predictive control (NMPC) scheme.
URI: http://dx.doi.org/10.1021/ie050904z
http://dspace.library.iitb.ac.in/xmlui/handle/10054/3871
http://hdl.handle.net/10054/3871
ISSN: 0888-5885
Appears in Collections:Article

Files in This Item:
There are no files associated with this item.


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