2024-03-29T05:18:32Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/1163292023-06-09T10:03:16Zcom_10803_183col_10803_229
TDX (Tesis Doctorals en Xarxa)
author
Fernández Canti, Rosa Ma.
authoremail
rfernandez@tsc.upc.edu
authoremailshow
false
director
Puig Cayuela, Vicenç
codirector
Blesa Izquierdo, Joaquim
authorsendemail
true
2013-06-11T12:02:09Z
2013-06-11T12:02:09Z
2013-02-07
http://hdl.handle.net/10803/116329
B. 16897-2013
In the Control Engineering field, the so-called Robust Identification techniques deal with the problem of obtaining not only a nominal model of the plant, but also an estimate of the uncertainty associated to the nominal model. Such model of uncertainty is typically characterized as a region in the parameter space or as an uncertainty band around the frequency response of the nominal model.
Uncertainty models have been widely used in the design of robust controllers and, recently, their use in model-based fault detection procedures is increasing. In this later case, consistency between new measurements and the uncertainty region is checked. When an inconsistency is found, the existence of a fault is decided.
There exist two main approaches to the modeling of model uncertainty: the deterministic/worst case methods and the stochastic/probabilistic methods. At present, there are a number of different methods, e.g., model error modeling, set-membership identification and non-stationary stochastic embedding. In this dissertation we summarize the main procedures and illustrate their results by means of several examples of the literature.
As contribution we propose a Bayesian methodology to solve the robust identification problem. The approach is highly unifying since many robust identification techniques can be interpreted as particular cases of the Bayesian framework. Also, the methodology can deal with non-linear structures such as the ones derived from the use of observers. The obtained Bayesian uncertainty models are used to detect faults in a quadruple-tank process and in a three-bladed wind turbine.
eng
A Bayesian approach to robust identification: application to fault detection
info:eu-repo/semantics/doctoralThesis info:eu-repo/semantics/publishedVersion
URL
https://www.tdx.cat/bitstream/10803/116329/2/TRFC1de1.pdf
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