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Isaksson, Alf J.
Alternative names
Publications (10 of 53) Show all publications
Isaksson, A., Sjöberg, J., Tornqvist, D., Ljung, L. & Kok, M. (2015). Using horizon estimation and nonlinear optimization for grey-box identification. Journal of Process Control, 30, 69-79
Open this publication in new window or tab >>Using horizon estimation and nonlinear optimization for grey-box identification
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2015 (English)In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 30, p. 69-79Article in journal (Refereed) Published
Abstract [en]

An established method for grey-box identification is to use maximum-likelihood estimation for the nonlinear case implemented via extended Kalman filtering. In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that, in the linear case, horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. For the nonlinear case two special cases are presented where the bias correction can be determined without approximation. A procedure how to approximate the bias correction for general nonlinear systems is also outlined. (C) 2015 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
System identification; State estimation; Parameter estimation; Optimization; Nonlinear systems; Kalman filtering; Moving horizon estimation; Model predictive control
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-120061 (URN)10.1016/j.jprocont.2014.12.008 (DOI)000356196200007 ()
Note

Funding Agencies|Swedish Foundation for Strategic Research (SSF) - as part of the Process Industry Centre Linkoping (PIC-LI); Swedish Agency for Innovation Systems (VINNOVA) through the ITEA 2 project MODRIO; Linnaeus Center CADICS - Swedish Research Council; ERC advanced grant LEARN - European Research Council [similar to267381]

Available from: 2015-07-06 Created: 2015-07-06 Last updated: 2017-12-04
Isaksson, A. J. (2013). Some aspects of industrial system identification. In: : . Paper presented at 10th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2013; Mumbai; India (pp. 153-159).
Open this publication in new window or tab >>Some aspects of industrial system identification
2013 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

The most important and time consuming part of an industrial application of control is the modelling. It may take 50 per cent or more of the entire project. Therefore a major challenge for a control systems supplier like ABB is to constantly try to decrease the engineering effort for modelling.

This paper discusses some different aspects of modelling and identification originating from application in many different industries such as pulp and paper, rolling mills, power plants and specialty chemicals.

Series
IFAC Proceedings Volumes, ISSN 1474-6670 ; 10
Keywords
System identification, modeling, grey-box identification, data mining, process control.
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
urn:nbn:se:liu:diva-104982 (URN)10.3182/20131218-3-IN-2045.00192 (DOI)
Conference
10th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2013; Mumbai; India
Available from: 2014-03-05 Created: 2014-03-05 Last updated: 2014-12-09Bibliographically approved
Rosander, P., Isaksson, A., Löfberg, J. & Forsman, K. (2012). Performance Analysis of Robust Averaging Level Control. In: Proceedings of the 2012 Conference on Chemical Process Control: . Paper presented at 2012 Conference on Chemical Process Control, Savannah, GA, USA, 11-13 January, 2012.
Open this publication in new window or tab >>Performance Analysis of Robust Averaging Level Control
2012 (English)In: Proceedings of the 2012 Conference on Chemical Process Control, 2012Conference paper, Published paper (Refereed)
Abstract [en]

Frequent inlet flow changes, especially in the same direction, typically cause problems for averaging level controllers. To obtain optimal flow filtering while being robust towards future inlet flow upsets closed loop robust MPC was used. Its performance and robustness is analyzed and compared to the optimal averaging level controller. The knowledge gained from the robust MPC exercise is also used to propose a robustification of the optimal controller. Both the analysis and the simulation results show that the robust controller obtains comparable flow filtering as the optimal controller even when inlet flow changes are sparse while handling frequent upsets considerably better

Keywords
Averaging level control, Nonlinear control, Surge tanks
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-95600 (URN)
Conference
2012 Conference on Chemical Process Control, Savannah, GA, USA, 11-13 January, 2012
Funder
Swedish Foundation for Strategic Research
Available from: 2013-07-10 Created: 2013-07-10 Last updated: 2013-07-10
Rosander, P., Isaksson, A., Löfberg, J. & Forsman, K. (2012). Practical Control of Surge Tanks Suffering from Frequent Inlet Flow Upsets. In: Proceedings of the 2nd IFAC Conference on Advances in PID Control: . Paper presented at 2nd IFAC Conference on Advances in PID Control, Brescia, Italy, 28-30 March, 2012 (pp. 258-263).
Open this publication in new window or tab >>Practical Control of Surge Tanks Suffering from Frequent Inlet Flow Upsets
2012 (English)In: Proceedings of the 2nd IFAC Conference on Advances in PID Control, 2012, p. 258-263Conference paper, Published paper (Refereed)
Abstract [en]

In the presence of frequent inlet flow upsets, tuning of averaging level controllers is typically quite complicated since not only the size of the individual steps but also the time in between the subsequent steps need to considered. One structured way to achieve optimal filtering for such a case is to use Robust Model Predictive Control. The robust MPC controller is, however, quite computationally demanding and not easy to implement. In this paper two linear controllers, which mimic the behavior of the robust MPC, are proposed. Tuning guidelines to avoid violation of the tank level constraints as well as to achieve optimal filtering are presented.

Keywords
Averaging level control, PI controllers, Surge tanks
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-95601 (URN)978-3-902823-18-2 (ISBN)
Conference
2nd IFAC Conference on Advances in PID Control, Brescia, Italy, 28-30 March, 2012
Available from: 2013-07-10 Created: 2013-07-10 Last updated: 2014-11-11
Peretzki, D., Isaksson, A., Carvalho Bittencourt, A. & Forsman, K. (2011). Data Mining of Historic Data for Process Identification. In: Proceedings of the 2011 AIChE Annual Meeting: . Paper presented at 2011 AIChE Annual Meeting, Minneapolis, MN, USA, 16-21 October, 2011 (pp. 1027-1033). American Institute of Chemical Engineers
Open this publication in new window or tab >>Data Mining of Historic Data for Process Identification
2011 (English)In: Proceedings of the 2011 AIChE Annual Meeting, American Institute of Chemical Engineers, 2011, p. 1027-1033Conference paper, Published paper (Refereed)
Abstract [en]

Performing experiments for system identication is often a time-consuming task which may also interfere with the process operation. With memory prices going down, it is more and more common that years of process data are stored (without compression) in a history database. The rationale for this work is that in such stored data there must already be intervals informative enough for system identication. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identication (rather than performing completely automatic system identication). For each loop, 4 stored variables are required; setpoint, manipulated variable, process output and mode of the controller.

The proposed method requires a minimum of knowledge of the process and is implemented in a simple and ecient recursive algorithm. The essential features of the method are the search for excitation of the input and output, followed by the estimation of a Laguerre model combined with a chi-square test to check that at least one estimated parameter is statistically signicant. The use of Laguerre models is crucial to handle processes with deadtime without explicit delay estimation. The method was tested on three years of data from more than 200 control loops. It was able to find all intervals in which known identication experiments were performed as well as many other useful intervals in closed/open loop operation.

Place, publisher, year, edition, pages
American Institute of Chemical Engineers, 2011
Keywords
Data mining, Data segmentation, System identification, Excitation, Condition numbers, Laguerre filters
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-95592 (URN)978-0-8169-1070-0 (ISBN)9781618397409 (ISBN)
Conference
2011 AIChE Annual Meeting, Minneapolis, MN, USA, 16-21 October, 2011
Funder
Swedish Foundation for Strategic Research
Available from: 2013-07-10 Created: 2013-07-10 Last updated: 2013-12-04
Peretzki, D., Isaksson, A., Carvalho Bittencourt, A. & Forsman, K. (2011). Data Mining of Historic Data for Process Identification. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Data Mining of Historic Data for Process Identification
2011 (English)Report (Other academic)
Abstract [en]

Performing experiments for system identication is often a time-consuming task which may also interfere with the process operation. With memory prices going down, it is more and more common that years of process data are stored (without compression) in a history database. The rationale for this work is that in such stored data there must already be intervals informative enough for system identication. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identication (rather than performing completely automatic system identication). For each loop, 4 stored variables are required; setpoint, manipulated variable, process output and mode of the controller.

The proposed method requires a minimum of knowledge of the process and is implemented in a simple and ecient recursive algorithm. The essential features of the method are the search for excitation of the input and output, followed by the estimation of a Laguerre model combined with a chi-square test to check that at least one estimated parameter is statistically signicant. The use of Laguerre models is crucial to handle processes with deadtime without explicit delay estimation. The method was tested on three years of data from more than 200 control loops. It was able to find all intervals in which known identication experiments were performed as well as many other useful intervals in closed/open loop operation.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. p. 9
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3039
Keywords
Data mining, Data segmentation, System identification, Excitation, Condition numbers, Laguerre filters
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-97980 (URN)LiTH-ISY-R-3039 (ISRN)
Funder
Swedish Foundation for Strategic Research
Available from: 2013-09-23 Created: 2013-09-23 Last updated: 2014-09-19Bibliographically approved
Rosander, P., Isaksson, A., Löfberg, J. & Forsman, K. (2011). Performance Analysis of Robust Averaging Level Control. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Performance Analysis of Robust Averaging Level Control
2011 (English)Report (Other academic)
Abstract [en]

Frequent inlet flow changes, especially in the same direction, typically cause problems for averaging level controllers. To obtain optimal flow filtering while being robust towards future inlet flow upsets closed loop robust MPC was used. Its performance and robustness is analyzed and compared to the optimal averaging level controller. The knowledge gained from the robust MPC exercise is also used to propose a robustification of the optimal controller. Both the analysis and the simulation results show that the robust controller obtains comparable flow filtering as the optimal controller even when inlet flow changes are sparse while handling frequent upsets considerably better.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. p. 8
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3030
Keywords
Averaging level control, Nonlinear control, Surge tanks
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-97965 (URN)LiTH-ISY-R-3030 (ISRN)
Funder
Swedish Foundation for Strategic Research
Available from: 2013-09-23 Created: 2013-09-23 Last updated: 2014-09-22Bibliographically approved
Rosander, P., Isaksson, A., Löfberg, J. & Forsman, K. (2011). Practical Control of Surge Tanks Suffering from Frequent Inlet Flow Upsets. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Practical Control of Surge Tanks Suffering from Frequent Inlet Flow Upsets
2011 (English)Report (Other academic)
Abstract [en]

In the presence of frequent inlet flow upsets, tuning of averaging level controllers is typically quite complicated since not only the size of the individual steps but also the time in between the subsequent steps need to considered. One structured way to achieve optimal filtering for such a case is to use Robust Model Predictive Control. The robust MPC controller is, however, quite computationally demanding and not easy to implement. In this paper two linear controllers, which mimic the behavior of the robust MPC, are proposed. Tuning guidelines to avoid violation of the tank level constraints as well as to achieve optimal filtering are presented.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. p. 9
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3036
Keywords
Averaging level control, PI controllers, Surge tanks
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-97977 (URN)LiTH-ISY-R-3036 (ISRN)
Available from: 2013-09-23 Created: 2013-09-23 Last updated: 2014-09-22Bibliographically approved
Rosander, P., Isaksson, A., Löfberg, J. & Forsman, K. (2011). Robust Averaging Level Control. In: Proceedings of the 2011 AIChE Annual Meeting: . Paper presented at 2011 AIChE Annual Meeting, Minnepolis, MN, USA, 16-21 October, 2011.
Open this publication in new window or tab >>Robust Averaging Level Control
2011 (English)In: Proceedings of the 2011 AIChE Annual Meeting, 2011Conference paper, Published paper (Refereed)
Abstract [en]

Frequent inlet flow changes typically cause problems for averaging level controllers. For a frequently changing inlet flow the upsets do not occur when the system is in steady state and the tank level at its set-point. For this reason the tuning of the level controller gets quite complicated, since not only the size of the upsets but also the time in between them relative to the hold up of the tank have to be considered. One way to obtain optimal flow filtering while directly accounting for future inlet flow upsets is to use closed-loop robust MPC, as proposed here. The behavior of the robust MPC controller differs from earlier proposed level controllers as it does not return the tank level to a fixed set-point following an inlet flow upset. Guidelines on the tuning of the controller is presented and its performance is compared to that of a previously proposed MPC approach.

Keywords
Averaging level control, Robust MPC, Surge tanks
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-95590 (URN)978-0-8169-1070-0 (ISBN)
Conference
2011 AIChE Annual Meeting, Minnepolis, MN, USA, 16-21 October, 2011
Funder
Swedish Foundation for Strategic Research
Available from: 2013-07-10 Created: 2013-07-10 Last updated: 2013-09-23
Rosander, P., Isaksson, A., Löfberg, J. & Forsman, K. (2011). Robust Averaging Level Control. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Robust Averaging Level Control
2011 (English)Report (Other academic)
Abstract [en]

Frequent inlet flow changes typically cause problems for averaging level controllers. For a frequently changing inlet flow the upsets do not occur when the system is in steady state and the tank level at its set-point. For this reason the tuning of the level controller gets quite complicated, since not only the size of the upsets but also the time in between them relative to the hold up of the tank have to be considered. One way to obtain optimal flow filtering while directly accounting for future inlet flow upsets is to use closed-loop robust MPC, as proposed here. The behavior of the robust MPC controller differs from earlier proposed level controllers as it does not return the tank level to a fixed set-point following an inlet flow upset. Guidelines on the tuning of the controller is presented and its performance is compared to that of a previously proposed MPC approach.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. p. 10
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3023
Keywords
Averaging level control, Robust MPC, Surge tanks
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-97961 (URN)LiTH-ISY-R-3023 (ISRN)
Funder
Swedish Foundation for Strategic Research
Available from: 2013-09-23 Created: 2013-09-23 Last updated: 2014-09-22Bibliographically approved
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