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Efficiency of Prediction Error and Instrumental Variable Methods for Closed-Loop Identification
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
1998 (Engelska)Ingår i: Proceedings of the 37th IEEE Conference on Decision and Control, 1998, s. 1287-1288 vol.2Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We study the efficiency of a number of closed-loop identification methods. Results will be given for methods based on the prediction error approach as well as those based on the instrumental variable approach. Moreover, interesting insights in the properties of a recently suggested subspace method for closed-loop identification are obtained by exploring the links between this method and the instrumental variable method.

Ort, förlag, år, upplaga, sidor
1998. s. 1287-1288 vol.2
Nyckelord [en]
Closed loop systems, Covariance matrices, Feedback, Linear systems, Parameter estimation
Nationell ämneskategori
Reglerteknik
Identifikatorer
URN: urn:nbn:se:liu:diva-91591DOI: 10.1109/CDC.1998.758456ISBN: 0-7803-4394-8 (tryckt)OAI: oai:DiVA.org:liu-91591DiVA, id: diva2:624997
Konferens
37th IEEE Conference on Decision and Control, Tampa, FL, USA, December, 1998
Tillgänglig från: 2013-06-03 Skapad: 2013-04-28 Senast uppdaterad: 2013-10-09
Ingår i avhandling
1. Closed-loop Identification: Methods, Theory, and Applications
Öppna denna publikation i ny flik eller fönster >>Closed-loop Identification: Methods, Theory, and Applications
1999 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

System identification deals with constructing mathematical models of dynamical systems from measured data. Such models have important applications in many technical and nontechnical areas, such as diagnosis, simulation, prediction, and control. The theme in this thesis is to study how the use of closed-loop data for identication of open-loop processes affects dierent identification methods. The focus is on prediction error methods for closed-loop identification and a main resultis that we show that most common methods correspond to diefferent parameterizations of the general prediction error method. This provides a unifying framework for analyzing the statistical properties of the different methods. Here we concentrate on asymptotic variance expressions for the resulting estimates and on explicit characterizations of the bias distribution for the different methods. Furthermore, we present and analyze a new method for closed-loop identification, called the projection method, which allows approximation of the open-loop dynamics in a fixed, user-specified frequency domain norm, even in the case of an unknown, nonlinear regulator.

In prediction error identification it is common to use some gradient-type search algorithm for the parameter estimation. A requirement is then that the predictor filters along with their derivatives are stable for all admissible values of the parameters. The standard output error and Box-Jenkins model structures cannot beused if the underlying system is unstable, since the predictor filters will generically be unstable under these circumstances. In the thesis, modified versions of these model structures are derived that are applicable also to unstable systems. Another way to handle the problems associated with output error identification of unstable systems is to implement the search algorithm using noncausal filtering. Several such approaches are also studied and compared.

Another topic covered in the thesis is the use of periodic excitation signals for time domain identification of errors-in-variables systems. A number of compensation strategies for the least-squares and total least-squares methods are suggested. The main idea is to use a nonparametric noise model, estimated directly from data, to whiten the noise and to remove the bias in the estimates.

"Identication for Control" deals specically with the problem of constructing models from data that are good for control. A main idea has been to try to match the identication and control criteria to obtain a control-relevant model fit. The use of closed-loop experiments has been an important tool for achieving this. We study a number of iterative methods for dealing with this problem and show how they can be implemented using the indirect method. Several problems with the iterative schemes are observed and it is argued that performing iterated identification experiments with the current controller in the loop is suboptimal. Related to this is the problem of designing the identification experiment so that the quality of the resulting model is maximized. Here we concentrate on minimizing the variance error and a main result is that we give explicit expressions for the optimal regulator and reference signal spectrum to use in the identification experiment in case both the input and the output variances are constrained

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 1999. s. 247
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 566
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:liu:diva-98129 (URN)91-7219-432-4 (ISBN)
Disputation
1999-03-31, C3, Hus C, Campus Valla, Linköpings universitet, Linköping, 10:15 (Engelska)
Handledare
Tillgänglig från: 2013-10-09 Skapad: 2013-09-30 Senast uppdaterad: 2024-01-08Bibliografiskt granskad

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