| 1. |
- Forssell, Urban, et al.
(författare)
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A Projection Method for Closed-Loop Identification
- 1998
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A new method for closed-loop identification that allows fitting the model to the data with arbitrary frequency weighting is described and analyzed. Just as the direct method, this new method is applicable to systems with arbitrary feedback mechanisms. This is in contrast to other methods, such as the indirect method and the two-stage method, that assume linear feedback. The finite sample behavior of the proposed method is illustrated in a simulation study.
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| 2. |
- Forssell, Urban, et al.
(författare)
-
A Projection Method for Closed-Loop Identification
- 1997
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A new method for closed-loop identification that allows fitting the model to the data with arbitrary frequency weighting is described and analyzed. Just as the direct method, this new method is applicable to systems with arbitrary feedback mechanisms. This is in contrast to other methods, such as the indirect method and the two-stage method, that assume linear feedback. The finite sample behavior of the proposed method is illustrated in a simulation study.
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| 3. |
- Forssell, Urban, et al.
(författare)
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An Alternative Motivation for the Indirect Approach to Closed-Loop Identification
- 1999
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 44:11, s. 2206-2209
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Tidskriftsartikel (refereegranskat)abstract
- Direct prediction error identification of systems operating in closed loop may lead to biased results due to the correlation between the input and the output noise. The authors study this error, what factors affect it, and how it may be avoided. In particular, the role of the noise model is discussed and the authors show how the noise model should be parameterized to avoid the bias. Apart from giving important insights into the properties of the direct method, this provides a nonstandard motivation for the indirect method.
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| 4. |
- Forssell, Urban
(författare)
-
Asymptotic Variance Expressions for Identified Black-Box Models
- 1998
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The asymptotic probability distribution of identified black-box transfer function models is studied. The main contribution is that we derive variance expressions for the real and imaginary parts of the identified models that are asymptotic in both the number of measurements and the model order. These expressions are considerably simpler than the corresponding ones that hold for fixed model orders, and yet they frequently approximate the true covariance well already with quite modest model orders. We illustrate the relevance of the asymptotic expressions by using them to compute uncertainty regions for the frequency response of an identified model.
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| 5. |
- Forssell, Urban
(författare)
-
Closed-loop Identification : Methods, Theory, and Applications
- 1999
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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
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| 6. |
- Forssell, Urban, et al.
(författare)
-
Closed-Loop Identification Revisited
- 1997
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Identification of systems operating in closed loop has long been of prime interest in industrial applications. The problem offers many possibilities, and also some fallacies, and a wide variety of approaches have been suggested, many quite recently. The purpose of the current contribution is to place most of these approaches in a coherent framework, thereby showing their connections and display similarities and differences in the asymptotic properties of the resulting estimates. The common framework is created by the basic prediction error method, and it is shown that most of the common methods correspond to different parameterizations of the dynamics and noise models. The so-called indirect methods, e.g., are indeed “direct” methods employing noise models that contain the regulator. The asymptotic properties of the estimates then follow from the general theory and take different forms as they are translated to the particular parameterizations. We also study a new projection approach to closed-loop identification with the advantage of allowing approximation of the open-loop dynamics in a given, and user-chosen frequency domain norm, even in the case of an unknown, nonlinear regulator.
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| 7. |
- Forssell, Urban, 1970-, et al.
(författare)
-
Combining Semi-Physical and Neural Network Modeling : An Example of Its Usefulness
- 1996
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper illustrates the power of combining semi-physical and neural network modeling in an application example. It is argued that some of the problems related to the use of neural networks, such as high dimensionality of the parameter space and problems with undesired local minima, can be alleviated by this approach.
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| 8. |
- Forssell, Urban, et al.
(författare)
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Combining Semi-Physical and Neural Network Modeling : An Example of Its Usefulness
- 1997
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Ingår i: Proceedings of the 11th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 0080425925 ; , s. 795-798
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper illustrates the power of combining semi-physical and neural network modeling in an application example. It is argued that some of the problems related to the use of neural networks, such as high dimensionality of the parameter space and problems with undesired local minima, can be alleviated by this approach.
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| 9. |
- Forssell, Urban, et al.
(författare)
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Comparison of Methods for Probabilistic Uncertainty Bounding
- 1999
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Ingår i: Proceedings of the 38th IEEE Conference on Decision and Control. - 0780352505 ; , s. 522-527 vol.1
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Konferensbidrag (refereegranskat)abstract
- The problem of computing probabilistic uncertainty regions for the frequency responses of identified models is studied. A novel method for uncertainty bounding that uses bootstrap is presented and compared to a classical method using estimated covariance information. It is shown that, with bootstrap, it is possible to compute realistic uncertainty regions that closely resemble those obtainable through Monte Carlo simulations.
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| 10. |
- Forssell, Urban, et al.
(författare)
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Efficiency of Prediction Error and Instrumental Variable Methods for Closed-loop Identification
- 1998
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Ingår i: Proceedings of the 37th IEEE Conference on Decision and Control. - Linköping : Linköping University Electronic Press. - 0780343948 ; , s. 1287-1288 vol.2
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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