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BETA
Hansson, Anders, Professor
Alternative names
Publications (10 of 116) Show all publications
Hansson, A. & Pakazad, S. K. (2018). Exploiting Chordality in Optimization Algorithms for Model Predictive Control (227ed.). In: Large-Scale and Distributed Optimization: (pp. 11-32). Springer, 2227
Open this publication in new window or tab >>Exploiting Chordality in Optimization Algorithms for Model Predictive Control
2018 (English)In: Large-Scale and Distributed Optimization, Springer, 2018, 227, Vol. 2227, p. 11-32Chapter in book (Refereed)
Abstract [en]

In this chapter we show that chordal structure can be used to devise efficient optimization methods for many common model predictive control problems. The chordal structure is used both for computing search directions efficiently as well as for distributing all the other computations in an interior-point method for solving the problem. The chordal structure can stem both from the sequential nature of the problem as well as from distributed formulations of the problem related to scenario trees or other formulations. The framework enables efficient parallel computations.

Place, publisher, year, edition, pages
Springer, 2018 Edition: 227
Series
Lecture Notes in Mathematics, ISSN 0075-8434 ; 2227
Keywords
Model predictive control; Quadratic programming; Chordal graphs; Message passing; Dynamic programming; Parallel computations
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-154756 (URN)10.1007/978-3-319-97478-1_2 (DOI)000458487300003 ()978-3-319-97478-1 (ISBN)978-3-319-97477-4 (ISBN)
Available from: 2019-02-26 Created: 2019-02-26 Last updated: 2019-03-04Bibliographically approved
Kok, M., Khoshfetrat Pakazad, S., Schön, T., Hansson, A. & Hol, J. (2016). A Scalable and Distributed Solution to the Inertial Motion Capture Problem. In: Proceedings of the 19th International Conference on Information Fusion: . Paper presented at 19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016 (pp. 1348-1355). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Scalable and Distributed Solution to the Inertial Motion Capture Problem
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2016 (English)In: Proceedings of the 19th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1348-1355Conference paper, Published paper (Refereed)
Abstract [en]

In inertial motion capture, a multitude of body segments are equipped with inertial sensors, consisting of 3D accelerometers and 3D gyroscopes. Using an optimization-based approach to solve the motion capture problem allows for natural inclusion of biomechanical constraints and for modeling the connection of the body segments at the joint locations. The computational complexity of solving this problem grows both with the length of the data set and with the number of sensors and body segments considered. In this work, we present a scalable and distributed solution to this problem using tailored message passing, capable of exploiting the structure that is inherent in the problem. As a proof-of-concept we apply our algorithm to data from a lower body configuration. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130555 (URN)000391273400178 ()978-0-9964-5274-8 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016
Projects
CADICSELLIITThe project Probabilistic modeling of dynamical systems (Contract number: 621- 2013-5524)
Funder
Swedish Research CouncilELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2016-08-16 Created: 2016-08-16 Last updated: 2017-02-03
Karami, F., Khoshfetrat Pakazad, S., Hansson, A. & Afshar, A. (2015). Automated Model Generation for Analysis of Large-scale Interconnected Uncertain Systems. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Automated Model Generation for Analysis of Large-scale Interconnected Uncertain Systems
2015 (English)Report (Other academic)
Abstract [en]

The first challenge in robustness analysis of large-scale interconnected uncertain systems is to provide a model of such systems in a standard-form that is required within different analysis frameworks. This becomes particularly important for large-scale systems, as analysis tools that can handle such systems heavily rely on the special structure within such model descriptions. We here propose an automated framework for providing such models of large-scale interconnected uncertain systems that are used in Integral Quadratic Constraint (IQC) analysis. Specifically, in this paper we put forth a methodological way to provide such models from a block-diagram and nested description of interconnected uncertain systems. We describe the details of this automated framework using an example.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. p. 20
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3087
Keywords
LFT, Automated model generation, Large-scale analysis, Interconnected Uncertain Systems
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-123376 (URN)LiTH-ISY-R-3087 (ISRN)
Available from: 2015-12-14 Created: 2015-12-14 Last updated: 2018-09-17Bibliographically approved
Khoshfetrat Pakazad, S., Andersen, M. S. & Hansson, A. (2015). Distributed solutions for loosely coupled feasibility problems using proximal splitting methods. Optimization Methods and Software, 30(1), 128-161
Open this publication in new window or tab >>Distributed solutions for loosely coupled feasibility problems using proximal splitting methods
2015 (English)In: Optimization Methods and Software, ISSN 1055-6788, E-ISSN 1029-4937, Vol. 30, no 1, p. 128-161Article in journal (Refereed) Published
Abstract [en]

In this paper, we consider convex feasibility problems (CFPs) where the underlying sets are loosely coupled, and we propose several algorithms to solve such problems in a distributed manner. These algorithms are obtained by applying proximal splitting methods to convex minimization reformulations of CFPs. We also put forth distributed convergence tests which enable us to establish feasibility or infeasibility of the problem distributedly, and we provide convergence rate results. Under the assumption that the problem is feasible and boundedly linearly regular, these convergence results are given in terms of the distance of the iterates to the feasible set, which are similar to those of classical projection methods. In case the feasibility problem is infeasible, we provide convergence rate results that concern the convergence of certain error bounds.

Place, publisher, year, edition, pages
Taylor & Francis, 2015
Keywords
feasible/infeasible convex feasibility problems, proximal splitting, distributed solution, flow feasibility problem
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-110124 (URN)10.1080/10556788.2014.902056 (DOI)000345371800006 ()
Available from: 2014-09-03 Created: 2014-09-03 Last updated: 2017-12-05
Khoshfetrat Pakazad, S., Hansson, A. & Andersen, M. S. (2014). Distributed Interior-point Method for Loosely Coupled Problems. In: : . Paper presented at 19th IFAC world congress, The International Federation of Automatic Control, Cape Town, South Africa, August 24-29, 2014.
Open this publication in new window or tab >>Distributed Interior-point Method for Loosely Coupled Problems
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we put forth distributed algorithms for solving loosely coupled unconstrained and constrained optimization problems. Such problems are usually solved using algorithms that are based on a combination of decomposition and first order methods. These algorithms are commonly very slow and require many iterations to converge. In order to alleviate this issue, we propose algorithms that combine the Newton and interior-point methods with proximal splitting methods for solving such problems. Particularly, the algorithm for solving unconstrained loosely coupled problems, is based on Newton's method and utilizes proximal splitting to distribute the computations for calculating the Newton step at each iteration. A combination of this algorithm and the interior-point method is then used to introduce a distributed algorithm for solving constrained loosely coupled problems. We also provide guidelines on how to implement the proposed methods efficiently and briefly discuss the properties of the resulting solutions.

National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-110126 (URN)
Conference
19th IFAC world congress, The International Federation of Automatic Control, Cape Town, South Africa, August 24-29, 2014
Available from: 2014-09-03 Created: 2014-09-03 Last updated: 2014-10-01
Khoshfetrat Pakazad, S., Hansson, A., Andersen, M. S. & Rantzer, A. (2014). Distributed Robustness Analysis of Interconnected Uncertain Systems Using Chordal Decomposition. In: Edward Boje and Xiaohua Xia (Ed.), Proceedings of the 19th IFAC World Congress, 2014: . Paper presented at 19th IFAC world congress, The International Federation of Automatic Control, Cape Town, South Africa, August 24-29, 2014 (pp. 2594-2599). International Federation of Automatic Control
Open this publication in new window or tab >>Distributed Robustness Analysis of Interconnected Uncertain Systems Using Chordal Decomposition
2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014 / [ed] Edward Boje and Xiaohua Xia, International Federation of Automatic Control , 2014, p. 2594-2599Conference paper, Published paper (Refereed)
Abstract [en]

Large-scale interconnected uncertain systems commonly have large state and uncertainty dimensions. Aside from the heavy computational cost of solving centralized robust stability analysis techniques, privacy requirements in the network can also introduce further issues. In this paper, we utilize IQC analysis for analyzing large-scale interconnected uncertain systems and we evade these issues by describing a decomposition scheme that is based on the interconnection structure of the system. This scheme is based on the so-called chordal decomposition and does not add any conservativeness to the analysis approach. The decomposed problem can be solved using distributed computational algorithms without the need for a centralized computational unit. We further discuss the merits of the proposed analysis approach using a numerical experiment.

Place, publisher, year, edition, pages
International Federation of Automatic Control, 2014
Series
World Congress, ISSN 1474-6670 ; Volume 19, Part 1
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-110127 (URN)10.3182/20140824-6-ZA-1003.01649 (DOI)978-3-902823-62-5 (ISBN)
Conference
19th IFAC world congress, The International Federation of Automatic Control, Cape Town, South Africa, August 24-29, 2014
Available from: 2014-09-03 Created: 2014-09-03 Last updated: 2015-05-19Bibliographically approved
Hansson, A. & Verhaegen, M. (2014). Distributed system identification with ADMM. In: Proceedings of the 53rd IEEE Conference on Decision and Control: . Paper presented at 53rd IEEE Conference on Decision and Control 15-17 Dec. 2014, Los Angeles, CA (pp. 290-295). Los Angeles
Open this publication in new window or tab >>Distributed system identification with ADMM
2014 (English)In: Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, 2014, p. 290-295Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents identification of both network connected systems as well as distributed systems governed by PDEs in the framework of distributed optimization via the Alternating Direction Method of Multipliers. This approach opens first the possibility to identify distributed models in a global manner using all available data sequences and second the possibility for a distributed implementation. The latter will make the application to large scale complex systems possible. In addition to outlining a new large scale identification method, illustrations are shown for identifying both network connected systems and discretized PDEs.

Place, publisher, year, edition, pages
Los Angeles: , 2014
Series
53rd IEEE Conference on Decision and Control, ISSN 0191-2216
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-129292 (URN)10.1109/CDC.2014.7039396 (DOI)978-1-4799-7745-1 (ISBN)978-1-4673-6088-3 (ISBN)
Conference
53rd IEEE Conference on Decision and Control 15-17 Dec. 2014, Los Angeles, CA
Available from: 2016-06-15 Created: 2016-06-15 Last updated: 2018-01-10
Thomas, J. & Hansson, A. (2014). Enumerative nonlinear model predictive control for linear induction motor using load observer. In: 2014 UKACC International Conference on Control, CONTROL 2014 - Proceedings: . Paper presented at 10th UKACC International Conference on Control, CONTROL 2014 (pp. 373-377). Institute of Electrical and Electronics Engineers Inc. ( 6915169)
Open this publication in new window or tab >>Enumerative nonlinear model predictive control for linear induction motor using load observer
2014 (English)In: 2014 UKACC International Conference on Control, CONTROL 2014 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2014, no 6915169, p. 373-377Conference paper, Published paper (Refereed)
Abstract [en]

Enumerative nonlinear model predictive control for speed tracking problem of linear induction motors has been presented in [1], where the authors show that this control scheme has better performance as compared to direct torque control. In this paper, the authors show that using a load observer for integral action, the performance can be further improved. Specifically simulation results show that a load observer results in better tracking properties and offers more robust control.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2014
Series
2014 UKACC International Conference on Control, CONTROL 2014 - Proceedings
Keywords
Integral Action; Inverter; Linear Induction Motor; Load Observer; Nonlinear Model Predictive Control; Speed Tracking Control
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-116789 (URN)10.1109/CONTROL.2014.6915169 (DOI)000352626000064 ()2-s2.0-84921520732 (Scopus ID)9781479950119 (ISBN)
Conference
10th UKACC International Conference on Control, CONTROL 2014
Available from: 2015-04-07 Created: 2015-04-02 Last updated: 2015-05-11
Wallin, R. & Hansson, A. (2014). Maximum likelihood estimation of linear SISO models subject to missing output data and missing input data. International Journal of Control, 87(11), 2354-2364
Open this publication in new window or tab >>Maximum likelihood estimation of linear SISO models subject to missing output data and missing input data
2014 (English)In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 87, no 11, p. 2354-2364Article in journal (Refereed) Published
Abstract [en]

In this paper we describe an approach to maximum likelihood estimation of linear single input single output (SISO) models when both input and output data are missing. The criterion minimised in the algorithms is the Euclidean norm of the prediction error vector scaled by a particular function of the covariance matrix of the observed output data. We also provide insight into when simpler and in general sub-optimal schemes are indeed optimal. The algorithm has been prototyped in MATLAB, and we report numerical results that support the theory.

Place, publisher, year, edition, pages
Taylor and Francis: STM, Behavioural Science and Public Health Titles, 2014
Keywords
system identification; maximum likelihood estimation; missing data
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-111470 (URN)10.1080/00207179.2014.913346 (DOI)000341955300012 ()
Available from: 2014-10-17 Created: 2014-10-17 Last updated: 2017-12-05
Verhaegen, M. & Hansson, A. (2014). Nuclear norm subspace identification (N2SID) for short data batches. In: Proceedings of IFAC 2014 World Congress: . Paper presented at Preprints of the 19th World Congress The International Federation of Automatic Control. Cape Town, South Africa. August 24-29, 2014 (pp. 9528-9533). Cape Town
Open this publication in new window or tab >>Nuclear norm subspace identification (N2SID) for short data batches
2014 (English)In: Proceedings of IFAC 2014 World Congress, Cape Town, 2014, p. 9528-9533Conference paper, Published paper (Refereed)
Abstract [en]

Subspace identification is revisited in the scope of nuclear norm minimization methods. It is shown that essential structural knowledge about the unknown data matrices in the data equation that relates Hankel matrices constructed from input and output data can be used in the first step of the numerical solution presented. The structural knowledge comprises the low rank property of a matrix that is the product of the extended observability matrix and the state sequence and the Toeplitz structure of the matrix of Markov parameters (of the system in innovation form). The new subspace identification method is referred to as the N2SID (twice the N of Nuclear Norm and SID for Subspace IDentification) method. In addition to include key structural knowledge in the solution it integrates the subspace calculation with minimization of a classical prediction error cost function. The nuclear norm relaxation enables us to perform such integration while preserving convexity. The advantages of N2SID are demonstrated in a numerical open- and closed-loop simulation study. Here a comparison is made with another widely used SID method, i.e. N4SID. The comparison focusses on the identification with short data batches, i.e. where the number of measurements is a small multiple of the system order.

Place, publisher, year, edition, pages
Cape Town: , 2014
Keywords
Subspace system identification, Nuclear norm optimization, Rank constraint, Short
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-129293 (URN)10.3182/20140824-6-ZA-1003.00386 (DOI)
Conference
Preprints of the 19th World Congress The International Federation of Automatic Control. Cape Town, South Africa. August 24-29, 2014
Available from: 2016-06-15 Created: 2016-06-15 Last updated: 2016-06-29
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