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Hagenblad, Anna
Publications (10 of 22) Show all publications
Hagenblad, A., Ljung, L. & Wills, A. (2009). Maximum Likelihood Identification of Wiener Models. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Maximum Likelihood Identification of Wiener Models
2009 (English)Report (Other academic)
Abstract [en]

The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will, in general, lead to biased estimates if there are other disturbances present than measurement noise. The implications of Bussgang's theorem in this context are also discussed. For the case with general disturbances, we derive the Maximum Likelihood method and show how it can be efficiently implemented. Comparisons between this new algorithm and the traditional approach, confirm that the new method is unbiased and also has superior accuracy.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. p. 9
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2902
Keywords
System identification, Nonlinearities, Wiener model, Maximum likelihood, Prediction error method
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56063 (URN)LiTH-ISY-R-2902 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-12Bibliographically approved
Hagenblad, A., Ljung, L. & Wills, A. (2009). Maximum Likelihood Identification of Wiener models: Journal Version. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Maximum Likelihood Identification of Wiener models: Journal Version
2009 (English)Report (Other academic)
Abstract [en]

The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will, in general, lead to biased estimates if there are other disturbances present than measurement noise. The implications of Bussgang's theorem in this context are also discussed. For the case with general disturbances, we derive the Maximum Likelihood method and show how it can be efficiently implemented. Comparisons between this new algorithm and the traditional approach, confirm that the new method is unbiased and also has superior accuracy.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. p. 15
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2903
Keywords
System identification, Nonlinearities, Wiener model, Maximum likelihood, Prediction error method
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56064 (URN)LiTH-ISY-R-2903 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-12Bibliographically approved
Ljung, L., Hagenblad, A. & Wills, A. (2008). Maximum Likelihood Identification of Wiener Models. In: Proceedings of the 17th IFAC World Congress. Paper presented at 17th IFAC World Congress, Seoul, South Korea, July, 2008 (pp. 2714-2719).
Open this publication in new window or tab >>Maximum Likelihood Identification of Wiener Models
2008 (English)In: Proceedings of the 17th IFAC World Congress, 2008, p. 2714-2719Conference paper, Published paper (Refereed)
Abstract [en]

The Wiener model is a block oriented model having a linear dynamicsystem followed by a static nonlinearity.The dominating approachto estimate the components of this model has been to minimize theerror between the simulated and the measured outputs. We show thatthis will in general lead to biased estimates if there is otherdisturbances present than measurement noise. The implications ofBussgangs theorem in this context are also discussed. For the casewith general disturbances we derive the Maximum Likelihood methodand show how it can be efficiently implemented. Comparisons betweenthis new algorithm and the traditional approach confirm that the newmethod is unbiased and also has superior accuracy.

Keywords
Sytem identification
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-89041 (URN)10.3182/20080706-5-KR-1001.00457 (DOI)978-3-902661-00-5 (ISBN)
Conference
17th IFAC World Congress, Seoul, South Korea, July, 2008
Available from: 2013-02-21 Created: 2013-02-19 Last updated: 2013-02-21
Hagenblad, A., Ljung, L. & Wills, A. (2008). Maximum Likelihood Identification of Wiener Models. Automatica, 44(11), 2697-2705
Open this publication in new window or tab >>Maximum Likelihood Identification of Wiener Models
2008 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 44, no 11, p. 2697-2705Article in journal (Refereed) Published
Abstract [en]

The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will, in general, lead to biased estimates if there are other disturbances present than measurement noise. The implications of Bussgang's theorem in this context are also discussed. For the case with general disturbances, we derive the Maximum Likelihood method and show how it can be efficiently implemented. Comparisons between this new algorithm and the traditional approach, confirm that the new method is unbiased and also has superior accuracy.

Keywords
System identification, Nonlinearities, Wiener model, Maximum likelihood, Prediction error method
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-16239 (URN)10.1016/j.automatica.2008.02.016 (DOI)
Available from: 2009-01-12 Created: 2009-01-09 Last updated: 2017-12-14
Hagenblad, A., Gustafsson, F. & Klein, I. (2004). A Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis. In: Proceedings of Reglermöte 2004. Paper presented at Reglermöte 2004, Göteborg, Sweden, May, 2004.
Open this publication in new window or tab >>A Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis
2004 (English)In: Proceedings of Reglermöte 2004, 2004Conference paper, Published paper (Other academic)
Abstract [en]

This paper compares two methods for fault detection and isolation in a stochastic setting. We assume additive faults on input and output signals, and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. The stochastic parity space approach is similar to a Kalman filter, but uses an FIR fiter, while the Kalman filter is IIR. This enables faster response to changes. The second method is to use PCA, principal component analysis. In this case no model is needed, but fault isolation will be more difficult. The methods are illustrated on a simulation model of an F-16 aircraft. The fault detection probabilities can be calculated explicitly for the parity space approach, and are verified by simulations. The simulations of the PCA method suggest that the residuals have similar fault detection and isolation capabilities as for the stochastic parity space approach.

Keywords
Fault detection, Fault isolation, Diagnosis, Kalman filtering, Adaptive filters, Linear systems, Parity space, Principal components analysis, PCA
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-23791 (URN)3309 (Local ID)3309 (Archive number)3309 (OAI)
Conference
Reglermöte 2004, Göteborg, Sweden, May, 2004
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-03-24
Hagenblad, A., Gustafsson, F. & Klein, I. (2004). A Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>A Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis
2004 (English)Report (Other academic)
Abstract [en]

This paper compares two methods for fault detection and isolation in a stochastic setting. We assume additive faults on input and output signals, and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. The stochastic parity space approach is similar to a Kalman filter, but uses an FIR fiter, while the Kalman filter is IIR. This enables faster response to changes. The second method is to use PCA, principal component analysis. In this case no model is needed, but fault isolation will be more difficult. The methods are illustrated on a simulation model of an F-16 aircraft. The fault detection probabilities can be calculated explicitly for the parity space approach, and are verified by simulations. The simulations of the PCA method suggest that the residuals have similar fault detection and isolation capabilities as for the stochastic parity space approach.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2004. p. 8
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2636
Keywords
Fault detection, Fault isolation, Diagnosis, Kalman filtering, Adaptive filters, Linear systems, Parity space, Principal components analysis, PCA
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-55812 (URN)LITH-ISY-R-2636 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-19Bibliographically approved
Hagenblad, A., Gustafsson, F. & Klein, I. (2003). A Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis. In: Proceedings of the 13th IFAC Symposium on System Identification: . Paper presented at 13th IFAC Symposium on System Identification, Rotterdam, The Netherlands, August, 2003.
Open this publication in new window or tab >>A Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis
2003 (English)In: Proceedings of the 13th IFAC Symposium on System Identification, 2003Conference paper, Published paper (Refereed)
Abstract [en]

This paper reviews and compares two methods for fault detection and isolation in a stochastic setting, assuming additive faults on input and output signals and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. This leads to Kalman filter like residual generators, but with a FIR filter rather than an IIR filter as for the Kalman filter. The second method is to use principal component analysis (PCA). The advantage is that no model or structural information about the dynamic system is needed, in contrast to the parity space approach. We explain how PCA works in terms of parity space relations. The methods are illustrated on a simulation model of an F-16 aircraft, where six different faults are considered. The result is that PCA has similar fault detection and isolation capabilities as the stochastic parity space approach.

Keywords
Fault detection, Fault isolation, Diagnosis, Kalman filtering, Adaptive filters, Linear systems, Parity space, Principal components analysis, PCA
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-90299 (URN)0080437095 (ISBN)
Conference
13th IFAC Symposium on System Identification, Rotterdam, The Netherlands, August, 2003
Available from: 2013-03-30 Created: 2013-03-24 Last updated: 2013-08-29
Hagenblad, A. (2002). PBL - något för alla?!. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>PBL - något för alla?!
2002 (Swedish)Report (Other academic)
Abstract [en]

Keywords: teaching, education, problem based learning, PBL

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2002. p. 5
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2412
Keywords
Teaching, Education, Problem based learning, PBL
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-55860 (URN)LiTH-ISY-R-2412 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-14Bibliographically approved
Hagenblad, A. (2001). PBL - något för alla?!. In: Proceedings of the 2001 Process och produkt: 5:e universitetspedagogiska konferensen vid Linköpings universitet. Paper presented at Process och produkt. 5:e universitetspedagogiska konferensen vid Linköpings universitet, Linköping, Sweden, November, 2001 (pp. 92-95). , 3
Open this publication in new window or tab >>PBL - något för alla?!
2001 (English)In: Proceedings of the 2001 Process och produkt: 5:e universitetspedagogiska konferensen vid Linköpings universitet, 2001, Vol. 3, p. 92-95Conference paper, Published paper (Other academic)
Abstract [en]

n/a

Keywords
Teaching, Education, Problem based learning, PBL
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-90843 (URN)
Conference
Process och produkt. 5:e universitetspedagogiska konferensen vid Linköpings universitet, Linköping, Sweden, November, 2001
Available from: 2013-04-12 Created: 2013-04-07 Last updated: 2013-04-12
Klein, I. & Hagenblad, A. (2001). Teaching Control Theory Using Problem Based Learning. In: Proceedings of the 12th Annual Conference on Innovations in Education for Electrical and Information Engineering. Paper presented at 12th Annual Conference on Innovations in Education for Electrical and Information Engineering, Nancy, Francy, May, 2001.
Open this publication in new window or tab >>Teaching Control Theory Using Problem Based Learning
2001 (English)In: Proceedings of the 12th Annual Conference on Innovations in Education for Electrical and Information Engineering, 2001Conference paper, Published paper (Refereed)
Abstract [en]

Problem Based Learning, PBL, has been used for several years, especially in the medical commmunity. At Linköping University, now also the program in Information Technology is taught using this method. We describe how PBL is used in a basic course in control theory, including linear algebra and Laplace transforms. The experience from the first three years of the course is promising. The students are in general more active and more motivated than students in traditional courses. The teachers spend roughly the same number of hours on the course, but the focus has shifted to more contact with the students.

Keywords
Problem Based Learning
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-90789 (URN)2-9516740-0-7 (ISBN)
Conference
12th Annual Conference on Innovations in Education for Electrical and Information Engineering, Nancy, Francy, May, 2001
Available from: 2013-04-16 Created: 2013-04-07 Last updated: 2013-04-16
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