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Khoshfetrat Pakazad, Sina
Alternative names
Publications (10 of 23) Show all publications
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. (2015). Divide and Conquer: Distributed Optimization and Robustness Analysis. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Divide and Conquer: Distributed Optimization and Robustness Analysis
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As control of large-scale complex systems has become more and more prevalent within control, so has the need for analyzing such controlled systems. This is particularly due to the fact that many of the control design approaches tend to neglect intricacies in such systems, e.g., uncertainties, time delays, nonlinearities, so as to simplify the design procedure.

Robustness analysis techniques allow us to assess the effect of such neglected intricacies on performance and stability. Performing robustness analysis commonly requires solving an optimization problem. However, the number of variables of this optimization problem, and hence the computational time, scales badly with the dimension of the system. This limits our ability to analyze large-scale complex systems in a centralized manner. In addition, certain structural constraints, such as privacy requirements or geographical separation, can prevent us from even forming the analysis problem in a centralized manner.

In this thesis, we address these issues by exploiting structures that are common in large-scale systems and/or their corresponding analysis problems. This enables us to reduce the computational cost of solving these problems both in a centralized and distributed manner. In order to facilitate distributed solutions, we employ or design tailored distributed optimization techniques. Particularly, we propose three distributed optimization algorithms for solving the analysis problem, which provide superior convergence and/or computational properties over existing algorithms. Furthermore, these algorithms can also be used for solving general loosely coupled optimization problems that appear in a variety of fields ranging from control, estimation and communication systems to supply chain management and economics.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. p. 330
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1676
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-117503 (URN)10.3384/diss.diva-117503 (DOI)978-91-7519-050-1 (ISBN)
Public defence
2015-06-11, Visionen, Hus B, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2015-05-04 Created: 2015-04-29 Last updated: 2019-11-15Bibliographically approved
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
Andersen, M. S., Khoshfetrat Pakazad, S., Hansson, A. & Rantzer, A. (2014). Robust stability analysis of sparsely interconnected uncertain systems. IEEE Transactions on Automatic Control, 59(8), 2151-2156
Open this publication in new window or tab >>Robust stability analysis of sparsely interconnected uncertain systems
2014 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 59, no 8, p. 2151-2156Article in journal (Refereed) Published
Abstract [en]

In this paper, we consider robust stability analysis of large-scale sparsely interconnected uncertain systems. By modeling the interconnections among the subsystems with integral quadratic constraints, we show that robust stability analysis of such systems can be performed by solving a set of sparse linear matrix inequalities. We also show that a sparse formulation of the analysis problem is equivalent to the classical formulation of the robustness analysis problem and hence does not introduce any additional conservativeness. The sparse formulation of the analysis problem allows us to apply methods that rely on efficient sparse factorization techniques, and our numerical results illustrate the effectiveness of this approach compared to methods that are based on the standard formulation of the analysis problem.

Place, publisher, year, edition, pages
IEEE, 2014
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-110125 (URN)10.1109/TAC.2014.2305934 (DOI)000342923700012 ()
Available from: 2014-09-03 Created: 2014-09-03 Last updated: 2017-12-05Bibliographically approved
Ohlsson, H., Chen, T., Khoshfetratpakazad, S., Ljung, L. & Sastry, S. S. (2014). Scalable anomaly detection in large homogeneous populations. Automatica, 50(5), 1459-1465
Open this publication in new window or tab >>Scalable anomaly detection in large homogeneous populations
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2014 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 5, p. 1459-1465Article in journal (Refereed) Published
Abstract [en]

Anomaly detection in large populations is a challenging but highly relevant problem. It is essentially a multi-hypothesis problem, with a hypothesis for every division of the systems into normal and anomalous systems. The number of hypothesis grows rapidly with the number of systems and approximate solutions become a necessity for any problem of practical interest. In this paper we take an optimization approach to this multi-hypothesis problem. It is first shown to be equivalent to a non-convex combinatorial optimization problem and then is relaxed to a convex optimization problem that can be solved distributively on the systems and that stays computationally tractable as the number of systems increase. An interesting property of the proposed method is that it can under certain conditions be shown to give exactly the same result as the combinatorial multi-hypothesis problem and the relaxation is hence tight.

Place, publisher, year, edition, pages
International Federation of Automatic Control (IFAC), 2014
Keywords
Anomaly detection; Outlier detection; Multi-hypothesis testing; Distributed optimization; System identification
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-108173 (URN)10.1016/j.automatica.2014.03.008 (DOI)000336779100015 ()
Available from: 2014-06-28 Created: 2014-06-26 Last updated: 2024-01-08
Khoshfetrat Pakazad, S., S. Andersen, M., Hansson, A. & Rantzer, A. (2013). Decomposition and Projection Methods for Distributed Robustness Analysis of Interconnected Uncertain Systems. In: : . Paper presented at 13th IFAC Symposium on Large Scale Complex Systems: Theory and Applications (pp. 194-199).
Open this publication in new window or tab >>Decomposition and Projection Methods for Distributed Robustness Analysis of Interconnected Uncertain Systems
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We consider a class of convex feasibility problems where the constraints that describe the feasible set are loosely coupled. These problems arise in robust stability analysis of large, weakly interconnected uncertain systems. To facilitate distributed implementation of robust stability analysis of such systems, we describe two algorithms based on decomposition and simultaneous projections. The first algorithm is a nonlinear variant of Cimmino's mean projection algorithm, but by taking the structure of the constraints into account, we can obtain a faster rate of convergence. The second algorithm is devised by applying the alternating direction method of multipliers to a convex minimization reformulation of the convex feasibility problem. We use numerical results to show that both algorithms require far less iterations than the accelerated nonlinear Cimmino algorithm.

National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-103912 (URN)10.3182/20130708-3-CN-2036.00008 (DOI)
Conference
13th IFAC Symposium on Large Scale Complex Systems: Theory and Applications
Available from: 2014-02-03 Created: 2014-02-03 Last updated: 2014-02-12Bibliographically approved
Garulli, A., Hansson, A., Khoshfetrat Pakazad, S., Masi, A. & Wallin, R. (2013). Robust finite-frequency H2 analysis of uncertain systems with application to flight comfort analysis. Control Engineering Practice, 21(6), 887-897
Open this publication in new window or tab >>Robust finite-frequency H2 analysis of uncertain systems with application to flight comfort analysis
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2013 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 21, no 6, p. 887-897Article in journal (Refereed) Published
Abstract [en]

In many applications, design or analysis is performed over a finite-frequency range of interest. The importance of the H2 norm highlights the necessity of computing this norm accordingly. This paper provides different methods for computing upper bounds of the robust finite-frequency H2 norm for systems with structured uncertainties. An application of the robust finite-frequency H2 norm for a comfort analysis problem of an aero-elastic model of an aircraft is also presented.

Place, publisher, year, edition, pages
Elsevier, 2013
Keywords
Robust H-2 norm, Uncertain systems, Robust control, Flight comfort analysis
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-94316 (URN)10.1016/j.conengprac.2013.02.003 (DOI)000318327900011 ()
Available from: 2013-06-24 Created: 2013-06-24 Last updated: 2017-12-06
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