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  • 1.
    Andersen, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Rantzer, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Distributed Robust Stability Analysis of Interconnected Uncertain Systems2012In: Proceedings of the 51st IEEE Conference on Decision and Control, 2012, p. 1548-1553Conference paper (Refereed)
    Abstract [en]

    This paper considers robust stability analysis of a large network of interconnected uncertain systems. To avoid analyzing the entire network as a single large, lumped system, we model the network interconnections with integral quadratic constraints. This approach yields a sparse linear matrix inequality which can be decomposed into a set of smaller, coupled linear matrix inequalities. This allows us to solve the analysis problem efficiently and in a distributed manner. We also show that the decomposed problem is equivalent to the original robustness analysis problem, and hence our method does not introduce additional conservativeness.

  • 2.
    Andersen, Martin S.
    et al.
    Technical University of Denmark, Lyngby, Denmark.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Rantzer, Anders
    Lund University, Sweden.
    Robust stability analysis of sparsely interconnected uncertain systems2014In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 59, no 8, p. 2151-2156Article in journal (Refereed)
    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.

    Download full text (pdf)
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  • 3.
    Annergren, Mariette
    et al.
    KTH Royal Institute of Technology.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control.
    Wahlberg, Bo
    KTH Royal Institute of Technology.
    A Distributed Primal-dual Interior-point Method for Loosely Coupled Problems Using ADMMManuscript (preprint) (Other academic)
    Abstract [en]

    In this paper we propose an efficient distributed algorithm for solving loosely coupled convex optimization problems. The algorithm is based on a primal-dual interior-point method in which we use the alternating direction method of multipliers (ADMM) to compute the primal-dual directions at each iteration of the method. This enables us to join the exceptional convergence properties of primal-dual interior-point methods with the remarkable parallelizability of ADMM. The resulting algorithm has superior computational properties with respect to ADMM directly applied to our problem. The amount of computations that needs to be conducted by each computing agent is far less. In particular, the updates for all variables can be expressed in closed form, irrespective of the type of optimization problem. The most expensive computational burden of the algorithm occur in the updates of the primal variables and can be precomputed in each iteration of the interior-point method. We verify and compare our method to ADMM in numerical experiments.

  • 4.
    Garulli, Andrea
    et al.
    University of Siena, Italy.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Masi, Alfio
    University of Siena, Italy.
    Wallin, Ragnar
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Robust Finite-Frequency H2 Analysis of Uncertain Systems2011Report (Other academic)
    Abstract [en]

    In many applications, design or analysis is performed over a finite frequency range of interest. The importance of the H2/robust H2 norm highlights the necessity of computing this norm accordingly. This paper provides different methods for computing upper bounds on 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.

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    FULLTEXT01
  • 5.
    Garulli, Andrea
    et al.
    Dipartimento di Ingegneria dell'Informazione Universita' degli Studi di Siena, Italy.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Masi, Alfio
    Dipartimento di Ingegneria dell'Informazione Universita' degli Studi di Siena, Italy.
    Wallin, Ragnar
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Robust finite-frequency H2 analysis of uncertain systems with application to flight comfort analysis2013In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 21, no 6, p. 887-897Article in journal (Refereed)
    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.

    Download full text (pdf)
    fulltext
  • 6. Karami, Farzaneh
    et al.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Afshar, Ahmad
    Automated Model Generation for Analysis of Large-scale Interconnected Uncertain Systems2015Report (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.

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  • 7. Order onlineBuy this publication >>
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Divide and Conquer: Distributed Optimization and Robustness Analysis2015Doctoral 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.

    List of papers
    1. Distributed solutions for loosely coupled feasibility problems using proximal splitting methods
    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
    2. A Distributed Primal-dual Interior-point Method for Loosely Coupled Problems Using ADMM
    Open this publication in new window or tab >>A Distributed Primal-dual Interior-point Method for Loosely Coupled Problems Using ADMM
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    In this paper we propose an efficient distributed algorithm for solving loosely coupled convex optimization problems. The algorithm is based on a primal-dual interior-point method in which we use the alternating direction method of multipliers (ADMM) to compute the primal-dual directions at each iteration of the method. This enables us to join the exceptional convergence properties of primal-dual interior-point methods with the remarkable parallelizability of ADMM. The resulting algorithm has superior computational properties with respect to ADMM directly applied to our problem. The amount of computations that needs to be conducted by each computing agent is far less. In particular, the updates for all variables can be expressed in closed form, irrespective of the type of optimization problem. The most expensive computational burden of the algorithm occur in the updates of the primal variables and can be precomputed in each iteration of the interior-point method. We verify and compare our method to ADMM in numerical experiments.

    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-117495 (URN)
    Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2015-05-19
    3. Distributed primal-€dual interior-point methods for solving tree-structured coupled convex problems using message-passing
    Open this publication in new window or tab >>Distributed primal-€dual interior-point methods for solving tree-structured coupled convex problems using message-passing
    2017 (English)In: Optimization Methods and Software, ISSN 1055-6788, E-ISSN 1029-4937, p. 1-35Article in journal (Refereed) Published
    Abstract [en]

    In this paper, we propose a distributed algorithm for solving coupled problems with chordal sparsity or an inherent tree structure which relies on primal–dual interior-point methods. We achieve this by distributing the computations at each iteration, using message-passing. In comparison to existing distributed algorithms for solving such problems, this algorithm requires far fewer iterations to converge to a solution with high accuracy. Furthermore, it is possible to compute an upper-bound for the number of required iterations which, unlike existing methods, only depends on the coupling structure in the problem. We illustrate the performance of our proposed method using a set of numerical examples.

    Place, publisher, year, edition, pages
    Taylor & Francis, 2017
    National Category
    Control Engineering Mathematics
    Identifiers
    urn:nbn:se:liu:diva-133995 (URN)10.1080/10556788.2016.1213839 (DOI)000399480200001 ()
    Note

    The previous status of this article was Manuscript and the working title was Distributed Primal-dual Interior-point Methods for Solving Loosely Coupled Problems Using Message Passing.

    Funding agencies: Swedish Department of Education within the ELLIIT project

    Available from: 2017-01-17 Created: 2017-01-17 Last updated: 2017-12-18Bibliographically approved
    4. Robust stability analysis of sparsely interconnected uncertain systems
    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
    5. Distributed Robustness Analysis of Interconnected Uncertain Systems Using Chordal Decomposition
    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
    6. Distributed Semidefinite Programming with Application to Large-scale System Analysis
    Open this publication in new window or tab >>Distributed Semidefinite Programming with Application to Large-scale System Analysis
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Distributed algorithms for solving coupled semidefinite programs (SDPs) commonly require manyiterations to converge. They also put high computational demand on the computational agents. In thispaper we show that in case the coupled problem has an inherent tree structure, it is possible to devisean efficient distributed algorithm for solving such problems. This algorithm can potentially enjoy thesame efficiency as centralized solvers that exploit sparsity. The proposed algorithm relies on predictorcorrectorprimal-dual interior-point methods, where we use a message-passing algorithm to compute thesearch directions distributedly. Message-passing here is closely related to dynamic programming overtrees. This allows us to compute the exact search directions in a finite number of steps. Furthermorethis number can be computed a priori and only depends on the coupling structure of the problem. Weuse the proposed algorithm for analyzing robustness of large-scale uncertain systems distributedly. Wetest the performance of this algorithm using numerical examples.

    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-117496 (URN)
    Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2015-05-19
    7. Robust finite-frequency H2 analysis of uncertain systems with application to flight comfort analysis
    Open this publication in new window or tab >>Robust finite-frequency H2 analysis of uncertain systems with application to flight comfort analysis
    Show others...
    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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  • 8. Order onlineBuy this publication >>
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Topics in Robustness Analysis2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this thesis, we investigate two problems in robustness analysis of uncertain systems with structured uncertainty. The first problem concerns the robust finite frequency range H2 analysis of such systems. Classical robust H2 analysis methods are based on upper bounds for the robust H2 norm of a system which are computed over the whole frequency range. These bounds can be overly conservative, and therefore, classical robust H2 analysis methods can produce misleading results for finite frequency range analysis. In the first paper in the thesis, we address this issue by providing two methods for computing upper bounds for the robust finite-frequency H2 norm of the system. These methods utilize finitefrequency Gramians and frequency partitioning to calculate upper bounds for the robust finite-frequency H2 norm of uncertain systems with structured uncertainty. We show the effectiveness of these algorithms using both theoretical and practical experiments.

    List of papers
    1. Robust Finite-Frequency H2 Analysis of Uncertain Systems
    Open this publication in new window or tab >>Robust Finite-Frequency H2 Analysis of Uncertain Systems
    Show others...
    2011 (English)Report (Other academic)
    Abstract [en]

    In many applications, design or analysis is performed over a finite frequency range of interest. The importance of the H2/robust H2 norm highlights the necessity of computing this norm accordingly. This paper provides different methods for computing upper bounds on 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
    Linköping: Linköping University Electronic Press, 2011. p. 29
    Series
    LiTH-ISY-R, ISSN 1400-3902 ; 3011
    Keywords
    Robust H2 norm, Uncertain systems, Robust control
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-72208 (URN)LiTH-ISY-R-3011 (ISRN)
    Available from: 2011-11-22 Created: 2011-11-22 Last updated: 2014-06-18Bibliographically approved
    2. Decomposition and Projection Methods for Distributed Robustness Analysis of Interconnected Uncertain Systems
    Open this publication in new window or tab >>Decomposition and Projection Methods for Distributed Robustness Analysis of Interconnected Uncertain Systems
    2011 (English)Report (Other academic)
    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 systems. To facilitate distributed implementation of robust stability analysis of such systems, we propose 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.

    Place, publisher, year, edition, pages
    Linköping: Linköping University Electronic Press, 2011. p. 24
    Series
    LiTH-ISY-R, ISSN 1400-3902 ; 3033
    Keywords
    Robust stability analysis, Convex feasibility problems, Projection algorithms, Decomposition, Distributed computing
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-72209 (URN)LiTH-ISYR- 3033 (ISRN)
    Funder
    eLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
    Available from: 2011-11-22 Created: 2011-11-22 Last updated: 2014-09-01Bibliographically approved
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    Topics in Robustness Analysis
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  • 9.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Andersen, Martin S.
    Technical University of Denmark, Kongens Lyngby, Denmark.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Distributed solutions for loosely coupled feasibility problems using proximal splitting methods2015In: Optimization Methods and Software, ISSN 1055-6788, E-ISSN 1029-4937, Vol. 30, no 1, p. 128-161Article in journal (Refereed)
    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.

    Download full text (pdf)
    fulltext
  • 10.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Andersen, Martin S.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Rantzer, Anders
    Lund University, Sweden.
    Decomposition and Projection Methods for Distributed Robustness Analysis of Interconnected Uncertain Systems2011Report (Other academic)
    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 systems. To facilitate distributed implementation of robust stability analysis of such systems, we propose 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.

    Download full text (pdf)
    FULLTEXT01
  • 11.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ankelhed, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Project Report - ArduPilot2011Report (Other academic)
    Abstract [en]

    Ardu software, for autopilot functionality has been used for several years for many different UAV platforms. The aim of this project is to use the same functionality for the plane platform existing in the department. Considering the mismatch of the existing platform with the previously used ones for Ardu autopilots it is of great importance to alter the software and hardware provided by Ardu in order to be able to fly the existing UAV.

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  • 12.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Andersen, Martin S.
    Technical University of Denmark, Lyngby, Denmark.
    Distributed Interior-point Method for Loosely Coupled Problems2014Conference 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.

  • 13.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Andersen, Martin S.
    University of California Los Angeles, USA.
    Rantzer, Anders
    Lund University, Sweden.
    Decomposition and Simultaneous Projection Methods for Convex Feasibility Problems with Application to robustness Analysis of Interconnected Uncertain Systems2011Report (Other academic)
    Abstract [en]

    In this paper a specific class of convex feasibility problems are considered and tailored algorithms to solve this class of problems are introduced. First, the Nonlinear Cimmino Algorithm is reviewed. Then motivated by the special structure of the problems at hand, a modification to this method is proposed. Next, another method for solving the dual problem of the provided problem is presented. This leads to similar update rules for the variables as in the modified Nonlinear Cimmino Algorithm. Then an application for the proposed algorithms on the robust stability analysis of large scale weakly interconnected systems is presented and the performance of the proposed methods are compared.

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  • 14.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Andersen, Martin S.
    Technical University of Denmark, Lyngby, Denmark.
    Rantzer, Anders
    Lund University, Sweden.
    Distributed Robustness Analysis of Interconnected Uncertain Systems Using Chordal Decomposition2014In: Proceedings of the 19th IFAC World Congress, 2014 / [ed] Edward Boje and Xiaohua Xia, International Federation of Automatic Control , 2014, p. 2594-2599Conference 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.

  • 15.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control.
    Andersen, Martin S.
    Technical University of Denmark.
    Rantzer, Anders
    Lund University.
    Distributed Semidefinite Programming with Application to Large-scale System AnalysisManuscript (preprint) (Other academic)
    Abstract [en]

    Distributed algorithms for solving coupled semidefinite programs (SDPs) commonly require manyiterations to converge. They also put high computational demand on the computational agents. In thispaper we show that in case the coupled problem has an inherent tree structure, it is possible to devisean efficient distributed algorithm for solving such problems. This algorithm can potentially enjoy thesame efficiency as centralized solvers that exploit sparsity. The proposed algorithm relies on predictorcorrectorprimal-dual interior-point methods, where we use a message-passing algorithm to compute thesearch directions distributedly. Message-passing here is closely related to dynamic programming overtrees. This allows us to compute the exact search directions in a finite number of steps. Furthermorethis number can be computed a priori and only depends on the coupling structure of the problem. Weuse the proposed algorithm for analyzing robustness of large-scale uncertain systems distributedly. Wetest the performance of this algorithm using numerical examples.

  • 16.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Garulli, Andrea
    University of Siena, Italy.
    On the Calculation of the Robust Finite Frequency H2 Norm2011Report (Other academic)
    Abstract [en]

    The robust H2 norm plays an important role in analysis and design in many fields. However, for many practical applications, design and analysis is based on finite frequency range. In this paper we review the concept of the robust finite frequency H2 norm, and we provide an algorithmic method for calculating an upper bound for the mentioned quantity.

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  • 17.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Garulli, Andrea
    University of Siena, Italy.
    On the Calculation of the Robust Finite Frequency H2 Norm2011In: Proceedings of the 18th IFAC World Congress, 2011, p. 3360-3365Conference paper (Refereed)
    Abstract [en]

    The robust H2 norm plays an important role in analysis and design in many fields. However, for many practical applications, design and analysis is based on finite frequency range. In this paper we review the concept of the robust finite frequency H2 norm, and we provide an algorithmic method for calculating an upper bound for the mentioned quantity.

  • 18.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sparse Control Using Sum-of-norms Regularized Model Predictive Control2013Conference paper (Refereed)
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    fulltext
  • 19.
    Khoshfetrat Pakazad, Sina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    S. Andersen, Martin
    Technical University of Denmark.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Rantzer, Anders
    Lund University.
    Decomposition and Projection Methods for Distributed Robustness Analysis of Interconnected Uncertain Systems2013Conference 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.

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  • 20.
    Kok, Manon
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Schön, Thomas
    Uppsala University, Sweden.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hol, Jeroen
    Xsens Technologies B.V., Enschede, Netherlands.
    A Scalable and Distributed Solution to the Inertial Motion Capture Problem2016In: Proceedings of the 19th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1348-1355Conference 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. 

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  • 21.
    Ohlsson, Henrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Chen, Tianshi
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sastry, Shankar
    University of California at Berkeley, USA.
    Distributed Change Detection2012In: Proceedings of the 16th IFAC Symposium on System Identification, 2012, p. 77-82Conference paper (Refereed)
    Abstract [en]

    Change detection has traditionally been seen as a centralized problem. Many change detection problems are however distributed in nature and the need for distributed change detection algorithms is therefore significant. In this paper a distributed change detection algorithm is proposed. The change detection problem is first formulated as a convex optimization problem and then solved distributively with the alternating direction method of multipliers (ADMM). To further reduce the computational burden on each sensor, a homotopy solution is also derived. The proposed method have interesting connections with Lasso and compressed sensing and the theory developed for these methods are therefore directly applicable.

  • 22.
    Ohlsson, Henrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Chen, Tianshi
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Khoshfetratpakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sastry, S. Shankar
    University of Calif Berkeley, CA 94720 USA .
    Scalable anomaly detection in large homogeneous populations2014In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 5, p. 1459-1465Article in journal (Refereed)
    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.

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  • 23.
    Wallin, Ragnar
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Garuli, Andrea
    Università di Siena, Italy.
    Masi, Alfio
    Università di Siena, Italy.
    Applications of IQC-Based Analysis Techniques for Clearance2012In: Optimization Based Clearance of Flight Control Laws: A Civil Aircraft Application / [ed] Andreas Varga, Anders Hansson and Guilhem Puyou, Springer Berlin/Heidelberg, 2012, p. 277-297Chapter in book (Refereed)
    Abstract [en]

    Results for stability analysis of the nonlinear rigid aircraft model and comfort and loads analysis of the integral aircraft model are presented in this chapter. The analysis is based on the theory for integral quadratic constraints and relies on linear fractional representations (LFRs) of the underlying closed-loop aircraft models. To alleviate the high computational demands associated with the usage of IQC based analysis to large order LFRs, two approaches have been employed aiming a trade-off between computational complexity and conservatism. First, the partitioning of the flight envelope in several smaller regions allows to use lower order LFRs in the analysis, and second, IQCs with lower computational demands have been used whenever possible. The obtained results illustrate the applicability of the IQCs based analysis techniques to solve highly complex analysis problems with an acceptable level of conservativeness.

1 - 23 of 23
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