liu.seSearch for publications in DiVA
Change search
Link to record
Permanent link

Direct link
BETA
Sjöberg, Johan
Publications (10 of 30) Show all publications
Liu, B., Sjöberg, J. & Laiho, A. (2016). Optimization-based radial active magnetic bearing controller design and verification for flexible rotors. Proceedings of the Institution of mechanical engineers. Part I, journal of systems and control engineering, 230(4), 339-351
Open this publication in new window or tab >>Optimization-based radial active magnetic bearing controller design and verification for flexible rotors
2016 (English)In: Proceedings of the Institution of mechanical engineers. Part I, journal of systems and control engineering, ISSN 0959-6518, E-ISSN 2041-3041, Vol. 230, no 4, p. 339-351Article in journal (Refereed) Published
Abstract [en]

Engineering costs, especially cost for controller design, are substantial and obstruct active magnetic bearings for broader industrial applications. An optimization-based active magnetic bearing controller design method is developed to solve this problem. Optimization criteria are selected to describe active magnetic bearing practical performance. Controller components are chosen considering that the parameters can be manually interpreted and modified on-site for commissioning. A multi-objective optimization toolbox can be used to tune the controller parameters automatically by minimizing the optimization criteria. The method has been verified within a controller design process for an active magnetic bearing levitated machine. With this method, engineering effort for controller design can be reduced significantly.

Place, publisher, year, edition, pages
SAGE PUBLICATIONS LTD, 2016
Keywords
Active magnetic bearing; flexible rotor; rotor dynamics; optimization; multi objectives
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-127751 (URN)10.1177/0959651815621675 (DOI)000373919600006 ()
Available from: 2016-05-12 Created: 2016-05-12 Last updated: 2017-11-30
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
Show others...
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
Linder, J., Enqvist, M., Gustafsson, F. & Sjöberg, J. (2014). Identifiability of physical parameters in systems with limited sensors. In: Proceedings of the 19th IFAC World Congress: . Paper presented at 19th IFAC World Congress, South Africa, Cape Town, August 2014. (pp. 6454-6459).
Open this publication in new window or tab >>Identifiability of physical parameters in systems with limited sensors
2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014, p. 6454-6459Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a method for estimating physical parameters using limited sensors is investigated. As a case study, measurements from an IMU are used for estimating the change in mass and the change in center of mass of a ship. The roll motion is studied and an instrumental variable method estimating the parameters of a transfer function from the tangential acceleration to the angular velocity is presented. It is shown that only a subset of the unknown parameters are identifiable simultaneously. A multi-stage identification approach is presented as a remedy for this. A limited simulation study is also presented to show the properties of the estimator. This shows that the method is indeed promising but that more work is needed to reduce the variance of the estimator.

Series
IFAC-PapersOnLine, ISSN 1474-6670 ; 47(3)
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-111052 (URN)10.3182/20140824-6-ZA-1003.02272 (DOI)
Conference
19th IFAC World Congress, South Africa, Cape Town, August 2014.
Projects
LINK-SIC
Available from: 2014-10-06 Created: 2014-10-06 Last updated: 2016-12-14
Sjöberg, J., Lindkvist, S., Linder, J. & Daneryd, A. (2012). Interactive Multiobjective Optimization for the Hot Rolling Process. In: Proceedings of 51st IEEE Conference on Decision and Control: . Paper presented at 51st IEEE Conference on Decision and Control, Maui, HI, USA, 10-13 December, 2012 (pp. 7030-7036).
Open this publication in new window or tab >>Interactive Multiobjective Optimization for the Hot Rolling Process
2012 (English)In: Proceedings of 51st IEEE Conference on Decision and Control, 2012, p. 7030-7036Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, multi-objective optimization is applied to the hot rolling process. It is modeled mostly using first principle models considering, for instance, the mass balance (or mass flow rate), the tensions in the material, the power requirements, the thermal field, and the microstructure of the material.

Two optimization formulations are considered. In the first case, both the grain size and the power consumption in the rolling process are minimized. It is shown that the result from a single-objective optimization formulation, i.e., where only one of the two objectives are considered, yields control schedules with poor performance for the other objective. Furthermore, the differences between optimal control schedules for different objectives are compared and analyzed. The second case is a design optimization problem where the optimal positioning of cooling pipes is considered. This study shows how the MOO framework can be used to systematically choose a good cooling pipe setup. 

The two studies shows that MOO can be a helpful tool when designing and running hot rolling processes. Furthermore, navigation among the Pareto optimal solutions is very useful when the user wants to learn how the control variables interact with the process.

Keywords
Interaction, Optimization, Hot rolling process
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-88914 (URN)10.1109/CDC.2012.6426292 (DOI)978-1-4673-2064-1 (ISBN)978-1-4673-2065-8 (ISBN)
Conference
51st IEEE Conference on Decision and Control, Maui, HI, USA, 10-13 December, 2012
Available from: 2013-02-18 Created: 2013-02-18 Last updated: 2016-02-05
Linder, J., Lindkvist, S. & Sjöberg, J. (2012). Two-Step Framework for Interactive Multi-Objective Optimization. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Two-Step Framework for Interactive Multi-Objective Optimization
2012 (English)Report (Other academic)
Abstract [en]

In many real-world optimization applications there are often a number of conflicting objective functions that are all important to optimize. The purpose of multiobjective optimization (MOO) is to give the decision maker(DM) an understanding of how these functions are conflicting and the possibility to choose an appropriate trade-off between them. There are multiple methods for solving MOO problems but the focus in this paper is on interactive methods. When the size and complexity of the MOO problem grows the time needed to find a solution is too long to yield a pleasant experience for the DM. In this paper, a method to replace the original MOO problem with an approximation is suggested to speed up the process. The approximation is created and used in a two-step framework which makes it possible to investigate the Pareto frontier in real-time and that can handle nonlinear and non-convex MOO problems with m objective functions. The first step generates a number of samples of the complete Pareto frontier which is sparse but dense enough for the approximation. The second stepis an interactive tool for the DM to use to continuously and in real-time navigate on the approximated Pareto set in both objective- and decision space. The tool is used to investigate the Pareto frontier and to find a preferred solution. A method of decomposing the approximated set into simplices has been developed using Delaunay triangulation. This methodis able to make a good approximation for sets that are non-convex. The method is also able to handle disconnected sets and holes. This makes it possible to change the feasible region in both decision- and objective space. The framework is demonstrated on three example problems that show the functionality and performance of the implemented framework.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. p. 26
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3043
Keywords
Multiobjective optimization, Approximating Pareto set, Interactive, Convex decomposition, Decision space navigation
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-97986 (URN)LiTH-ISY-R-3043 (ISRN)
Available from: 2013-09-23 Created: 2013-09-23 Last updated: 2014-08-29Bibliographically approved
Isaksson, A., Törnqvist, D., Sjöberg, J. & Ljung, L. (2010). Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization
2010 (English)Report (Other academic)
Abstract [en]

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 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. A procedure how to approximate the bias correction for nonlinear systems is outlined.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. p. 9
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2963
Keywords
Grey-box, Identification, Estimation, Nonlinear, Optimization
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-97605 (URN)LiTH-ISY-R-2963 (ISRN)
Available from: 2013-09-17 Created: 2013-09-17 Last updated: 2014-08-11Bibliographically approved
Isaksson, A., Törnqvist, D., Sjöberg, J. & Ljung, L. (2009). Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization. In: Proceedings of the 41st ISCIE International Symposium on Stochastic Systems: . Paper presented at 41st ISCIE International Symposium on Stochastic Systems, Kobe, Japan, 13-14 November, 2009 (pp. 1-6). Institute of Systems, Control and Information Engineers
Open this publication in new window or tab >>Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization
2009 (English)In: Proceedings of the 41st ISCIE International Symposium on Stochastic Systems, Institute of Systems, Control and Information Engineers , 2009, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

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 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. A procedure how to approximate the bias correction for nonlinear systems is outlined.

Place, publisher, year, edition, pages
Institute of Systems, Control and Information Engineers, 2009
Keywords
Grey-box, Identification, Estimation, Nonlinear, Optimization
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-95544 (URN)9784915740473 (ISBN)
Conference
41st ISCIE International Symposium on Stochastic Systems, Kobe, Japan, 13-14 November, 2009
Available from: 2013-07-07 Created: 2013-07-07 Last updated: 2013-12-04
Sjöberg, J. (2008). Optimal Control and Model Reduction of Nonlinear DAE Models. (Doctoral dissertation). : Institutionen för systemteknik
Open this publication in new window or tab >>Optimal Control and Model Reduction of Nonlinear DAE Models
2008 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

In this thesis, different topics for models that consist of both differential and algebraic equations are studied. The interest in such models, denoted DAE models, have increased substantially during the last years. One of the major reasons is that several modern object-oriented modeling tools used to model large physical systems yield models in this form. The DAE models will, at least locally, be assumed to be described by a decoupled set of ordinary differential equations and purely algebraic equations. In theory, this assumption is not very restrictive because index reduction techniques can be used to rewrite rather general DAE models to satisfy this assumption.

One of the topics considered in this thesis is optimal feedback control. For state-space models, it is well-known that the Hamilton-Jacobi-Bellman equation (HJB) can be used to calculate the optimal solution. For DAE models, a similar result exists where a Hamilton-Jacobi-Bellman-like equation is solved. This equation has an extra term in order to incorporate the algebraic equations, and it is investigated how the extra term must be chosen in order to obtain the same solution from the different equations.

A problem when using the HJB to find the optimal feedback law is that it involves solving a nonlinear partial differential equation. Often, this equation cannot be solved explicitly. An easier problem is to compute a locally optimal feedback law. For analytic nonlinear time-invariant state-space models, this problem was solved in the 1960's, and in the 1970's the time-varying case was solved as well. In both cases, the optimal solution is described by convergent power series. In this thesis, both of these results are extended to analytic DAE models.

Usually, the power series solution of the optimal feedback control problem consists of an infinite number of terms. In practice, an approximation with a finite number of terms is used. A problem is that for certain problems, the region in which the approximate solution is accurate may be small. Therefore, another parametrization of the optimal solution, namely rational functions, is studied. It is shown that for some problems, this parametrization gives a substantially better result than the power series approximation in terms of approximating the optimal cost over a larger region.

A problem with the power series method is that the computational complexity grows rapidly both in the number of states and in the order of approximation. However, for DAE models where the underlying state-space model is control-affine, the computations can be simplified. Therefore, conditions under which this property holds are derived.

Another major topic considered is how to include stochastic processes in nonlinear DAE models. Stochastic processes are used to model uncertainties and noise in physical processes, and are often an important part in for example state estimation. Therefore, conditions are presented under which noise can be introduced in a DAE model such that it becomes well-posed. For well-posed models, it is then discussed how particle filters can be implemented for estimating the time-varying variables in the model.

The final topic in the thesis is model reduction of nonlinear DAE models. The objective with model reduction is to reduce the number of states, while not affecting the input-output behavior too much. Three different approaches are studied, namely balanced truncation, balanced truncation using minimization of the co-observability function and balanced residualization. To compute the reduced model for the different approaches, a method originally derived for nonlinear state-space models is extended to DAE models.

Place, publisher, year, edition, pages
Institutionen för systemteknik, 2008. p. 220
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1166
Keywords
DAE Models, Optimal Control, Model Reduction
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-11345 (URN)978-91-7393-964-5 (ISBN)
Public defence
2008-04-18, Visionen, B-building, Campus Valla, Linköping University, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2008-03-28 Created: 2008-03-28 Last updated: 2009-02-27
Sjöberg, J. & Glad, T. (2008). Power Series Solution of the Hamilton-Jacobi-Bellman Equation for DAE Models with a Discounted Cost. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Power Series Solution of the Hamilton-Jacobi-Bellman Equation for DAE Models with a Discounted Cost
2008 (English)Report (Other academic)
Abstract [en]

This paper considers infinite horizon optimal feedback control of nonlinear models with discounted cost. The paper includes two extensions of existing results about optimal feedback control. First, it is proven that for real analytic statespace models, a time-invariant real analytic feedback solution exists, even when the cost function includes a discount factor, provided certain regularity conditions. Second, the result is generalized to nonlinear DAE models. The feedback solution is valid in a neighborhood of the origin. In both cases, explicit formulas for the series expansions of the cost function and control law are given.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2850
Keywords
Optimal control, Series solution, Discounted cost, DAE
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56169 (URN)LiTH-ISY-R-2850 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-02Bibliographically approved
Sjöberg, J. & Glad, T. (2008). Power Series Solution of the Hamilton-Jacobi-Bellman Equation for DAE Models with a Discounted Cost. In: Proceedings of the 47th IEEE Conference on Decision and Control. Paper presented at 47th IEEE Conference on Decision and Control, Cancun, Mexico, December, 2008 (pp. 4761-4766).
Open this publication in new window or tab >>Power Series Solution of the Hamilton-Jacobi-Bellman Equation for DAE Models with a Discounted Cost
2008 (English)In: Proceedings of the 47th IEEE Conference on Decision and Control, 2008, p. 4761-4766Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers infinite horizon optimal feedback control of nonlinear models with discounted cost. The paper includes two extensions of existing results about optimal feedback control. First, it is proven that for real analytic statespace models, a time-invariant real analytic feedback solution exists, even when the cost function includes a discount factor, provided certain regularity conditions. Second, the result is generalized to nonlinear DAE models. The feedback solution is valid in a neighborhood of the origin. In both cases, explicit formulas for the series expansions of the cost function and control law are given.

Keywords
Optimal control, Series solution, Discounted cost, DAE
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-44426 (URN)10.1109/CDC.2008.4739422 (DOI)76608 (Local ID)978-1-4244-3124-3 (ISBN)978-1-4244-3123-6 (ISBN)76608 (Archive number)76608 (OAI)
Conference
47th IEEE Conference on Decision and Control, Cancun, Mexico, December, 2008
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-02-20
Organisations

Search in DiVA

Show all publications