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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 .
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Vandenberghe, Lieven
    University of California (UCLA), Los Angeles, CA, USA.
    Reduced-Complexity Semidefinite Relaxations of Optimal Power Flow Problems2014In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 29, no 4, p. 1855-1863Article in journal (Refereed)
    Abstract [en]

    We propose a new method for generating semidefinite relaxations of optimal power flow problems. The method is based on chordal conversion techniques: by dropping some equality constraints in the conversion, we obtain semidefinite relaxations that are computationally cheaper, but potentially weaker, than the standard semidefinite relaxation. Our numerical results show that the new relaxations often produce the same results as the standard semidefinite relaxation, but at a lower computational cost.

  • 3.
    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.

  • 4.
    Ankelhed, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    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.
    A Partially Augmented Lagrangian Method for Low Order H-Infinity Controller Synthesis Using Rational Constraints2011Report (Other academic)
    Abstract [en]

    When designing robust controllers, H-infinity synthesis is a common tool touse. The controllers that result from these algorithms are typically of very high order, which complicates implementation. However, if a constraint on the maximum order of the controller is set, that is lower than the order of the (augmented) system, the problem becomes nonconvex and it is relatively hard to solve. These problems become very complex, even when the order of the system is low.

    The approach used in this work is based on formulating the constraint onthe maximum order of the controller as a polynomial (or rational) equation.This equality constraint is added to the optimization problem of minimizingan upper bound on the H-innity norm of the closed loop system subjectto linear matrix inequality (LMI) constraints. The problem is then solvedby reformulating it as a partially augmented Lagrangian problem where theequality constraint is put into the objective function, but where the LMIsare kept as constraints.

    The proposed method is evaluated together with two well-known methodsfrom the literature. The results indicate that the proposed method hascomparable performance in most cases, especially if the synthesized con-troller has many parameters, which is the case if the system to be controlledhas many input and output signals.

  • 5.
    Ankelhed, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    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.
    A Partially Augmented Lagrangian Method for Low Order H-Infinity Controller Synthesis Using Rational Constraints2012In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 57, no 11, p. 2901-2905Article in journal (Refereed)
    Abstract [en]

    This technical note proposes a method for low order H-infinity synthesis where the constraint on the order of the controller is formulated as a rational equation. The resulting nonconvex optimization problem is then solved by applying a partially augmented Lagrangian method. The proposed method is evaluated together with two well-known methods from the literature. The results indicate that the proposed method has comparable performance and speed.

  • 6.
    Ankelhed, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    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.
    A Primal-Dual Method for Low Order H-Infinity Controller Synthesis2010In: Proceedings of Reglermöte 2010, Lund, 2010Conference paper (Other academic)
    Abstract [en]

    When designing robust controllers, H-infinity synthesis is a common tool to use. The controllers that result from these algorithms are typically of very high order, which complicates implementation. However, if a constraint on the maximum order of the controller is set, that is lower than the order of the (augmented) system, the problem becomes nonconvex and it is relatively hard to solve. These problems become very complex, even when the order of the system is low.

    The approach used in this work is based on formulating the constraint on the maximum order of the controller as a polynomial (or rational) equation. By using the fact that the polynomial (or rational) is non-negative on the feasible set, the problem is reformulated as an optimization problem where the nonconvex function is to be minimized over a convex set defined by linear matrix inequalities.

    The proposed method is evaluated together with a well-known method from the literature. The results indicate that the proposed method performs slightly better.

  • 7.
    Ankelhed, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    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.
    A Primal-Dual Method for Low Order H-Infinity Controller Synthesis2010Report (Other academic)
    Abstract [en]

    When designing robust controllers, H-infinity synthesis is a common tool to use. The controllers that result from these algorithms are typically of very high order, which complicates implementation. However, if a constraint on the maximum order of the controller is set, that is lower than the order of the (augmented) system, the problem becomes nonconvex and it is relatively hard to solve. These problems become very complex, even when the order of the system is low.

    The approach used in this work is based on formulating the constraint on the maximum order of the controller as a polynomial (or rational) equation. By using the fact that the polynomial (or rational) is non-negative on the feasible set, the problem is reformulated as an optimization problem where the nonconvex function is to be minimized over a convex set defined by linear matrix inequalities.

    The proposed method is evaluated together with a well-known method from the literature. The results indicate that the proposed method performs slightly better.

  • 8.
    Ankelhed, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    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.
    A Primal-Dual Method for Low Order H-Infinity Controller Synthesis2009In: Proceedings of the 48th IEEE Conference on Decision and Control held jointly with the 28th Chinese Control Conference, IEEE , 2009, p. 6674-6679Conference paper (Refereed)
    Abstract [en]

    When designing robust controllers, H-infinity synthesisis a common tool to use. The controllers that result from these algorithms are typically of very high order, which complicates implementation. However, if a constraint on the maximum order of the controller is set, that is lower than the order of the (augmented) system, the problem becomes nonconvex and it is relatively hard to solve. These problems become very complex,even when the order of the system is low.

    The approach used in this work is based on formulating the constraint on the maximum order of the controller as a polynomial (or rational) equation. By using the fact that the polynomial (or rational) is non-negative on the feasible set, the problem is reformulated as an optimization problem where the nonconvex function is to be minimized over a convex set defined by linear matrix inequalities.

    The proposed method is evaluated together with a wellknown method from the literature. The results indicate that the proposed method performs slightly better.

  • 9.
    Ankelhed, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    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.
    A Quasi-Newton Interior Point Method for Low Order H-Infinity Controller Synthesis2011In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 56, no 6, p. 1462-1467Article in journal (Refereed)
    Abstract [en]

    This technical note proposes a method for low order H-infinity synthesis where the constraint on the order of the controller is formulated as a rational equation. The resulting nonconvex optimization problem is then solved by applying a quasi-Newton primal-dual interior point method. The proposed method is evaluated together with a well-known method from the literature. The results indicate that the proposed method has comparable performance and speed.

  • 10.
    Ankelhed, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    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.
    Additional Numerical Results for the Quasi-Newton Interior Point Method for Low Order H-Infinity Controller Synthesis2010Report (Other academic)
    Abstract [en]

    Here we present numerical results and timings obtained using our quasi-Newton interior point method on a set of 44 systems. We were not able to include these results in the article due to limited amount of space. Also results from our evaluation of HIFOO on the same systems are included.

  • 11.
    Ankelhed, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    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.
    Suboptimal Model Reduction using LMIs with Convex Constraints2006Report (Other academic)
    Abstract [en]

    An approach to model reduction of LTI systems using Linear Matrix Inequalities (LMIs) in an H-infinity framework is presented, where non-convex constraints are replaced with stricter convex constraints thus making it suboptimal. The presented algorithms are compared with the Optimal Hankel reduction algorithm, and are shown to achieve better results (i.elower H-infinity errors) in cases where some of the Hankel singular values are close, but not equal to each other.

  • 12.
    Ardeshiri, Tohid
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Löfberg, Johan
    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.
    Convex Optimization Approach for Time-Optimal Path Tracking of Robots with Speed Dependent Constraints2010Report (Other academic)
    Abstract [en]

    The task of generating time optimal trajectories for a six degrees of freedom industrial robot is discussed and an existing convex optimization formulation of the problem is extended to include new types of constraints. The new constraints are speed dependent and can be motivated from physical modeling of the motors and the drive system. It is shown how the speed dependent constraints should be added in order to keep the convexity of the overall problem. A method to, conservatively, approximate the linear speed dependent constraints by a convex constraint is also proposed. A numerical example proves versatility of the extension proposed in this paper.

  • 13.
    Ardeshiri, Tohid
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Löfberg, Johan
    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.
    Convex Optimization Approach for Time-Optimal Path Tracking of Robots with Speed Dependent Constraints2011In: Proceedings of the 18th IFAC World Congress, IFAC , 2011, p. 14648-14653Conference paper (Refereed)
    Abstract [en]

    The task of generating time optimal trajectories for a six degrees of freedom industrial robot is discussed and an existing convex optimization formulation of the problem is extended to include new types of constraints. The new constraints are speed dependent and can be motivated from physical modeling of the motors and the drive system. It is shown how the speed dependent constraints should be added in order to keep the convexity of the overall problem. A method to, conservatively, approximate the linear speed dependent constraints by a convex constraint is also proposed. A numerical example proves versatility of the extension proposed in this paper.

  • 14.
    Axehill, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Fredrik
    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.
    A Preprocessing Algorithm Applicable to the Multiuser Detection Problem2005In: Proceedings of Radiovetenskap och Kommunikation 2005, 2005Conference paper (Other academic)
    Abstract [en]

    In this paper a preprocessing algorithm for binary quadratic programming problems is presented. For some types of binary quadratic programming problems, the algorithm can compute the optimal value for some or all integer variables without approximations in polynomial time. When the optimal multiuser detection problem is formulated as a maximum likelihood problem, a binary quadratic programming problem has to be solved. Fortunately, the low correlation between different users in the multiuser detection problem enables the use of the preprocessing algorithm. Simulations show that the preprocessing algorithm is able to compute almost all variables in the problem, even though the system is heavily loaded and affected by noise.

  • 15.
    Axehill, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Fredrik
    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.
    A Preprocessing Algorithm Applicable to the Multiuser Detection Problem2005Report (Other academic)
    Abstract [en]

    In this paper a preprocessing algorithm for binary quadratic programming problems is presented. For some types of binary quadratic programming problems, the algorithm can compute the optimal value for some or all integer variables without approximations in polynomial time. When the optimal multiuser detection problem is formulated as a maximum likelihood problem, a binary quadratic programming problem has to be solved. Fortunately, the low correlation between different users in the multiuser detection problem enables the use of the preprocessing algorithm. Simulations show that the preprocessing algorithm is able to compute almost all variables in the problem, even though the system is heavily loaded and affected by noise.

  • 16.
    Axehill, Daniel
    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.
    A Dual Gradient Projection Quadratic Programming Algorithm Tailored for Mixed Integer Predictive Control2008Report (Other academic)
    Abstract [en]

    The objective of this work is to derive a Mixed Integer Quadratic Programming algorithm tailored for Model Predictive Control for hybrid systems. The Mixed Integer Quadratic Programming algorithm is built on the branch and bound method, where Quadratic Programming relaxations of the original problem are solved in the nodes of a binary search tree. The difference between these subproblems is often small and therefore it is interesting to be able to use a previous solution as a starting point in a new subproblem. This is referred to as a warm start of the solver. Because of its warm start properties, an algorithm that works similar to an active set method is desired. A drawback with classical active set methods is that they often require many iterations in order to find the active set in optimum. So-called gradient projection methods are known to be able to identify this active set very fast. In the algorithm presented in this report, an algorithm built on gradient projection and projection of a Newton search direction onto the feasible set is used. It is a variant of a previously presented algorithm by the authors and makes it straightforward to utilize the previous result, where it is shown how the Newton search direction for the dual MPC problem can be computed very efficiently using Riccati recursions. As in the previous work, this operation can be performed with linear computational complexity in the prediction horizon. Moreover, the gradient computation used in the gradient projection part of the algorithm is also tailored for the problem in order to decrase the computational complexity. Furthermore, is is shown how a Riccati recursion still can be useful in the case when the system of equations for the ordinary search directino is inconsistent. In numerical experiments, the algorithm shows good performance, and it seems like the gradient projection strategy efficiently cuts down the number of Newton steps necessary to compute in order to reach the solution. When the algorithm is used as a part of an MIQP solver for hybrid MPC, the performance is still very good for small problems. However, for more difficult problems, there still seems to be some more work to do in order to get the performance of the commercial state-of-the-art solver CPLEX.

  • 17.
    Axehill, Daniel
    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.
    A Dual Gradient Projection Quadratic Programming Algorithm Tailored for Model Predictive Control2008In: Proceedings of Reglermöte 2008, 2008, p. 202-209Conference paper (Other academic)
    Abstract [en]

    The objective of this work is to derive a QPalgorithm tailored for MPC. More specific, the primary targetapplication is MPC for discrete-time hybrid systems. A desiredproperty of the algorithm is that warm starts should be possibleto perform efficiently. This property is very important for online linear MPC, and it is crucial in branch and bound forhybrid MPC. In this paper, a dual active set-like QP methodwas chosen because of its warm start properties. A drawbackwith classical active set methods is that they often requiremany iterations in order to find the active set in optimum.Gradient projection methods are methods known to be ableto identify this active set very fast and such a method wastherefore chosen in this work. The gradient projection methodwas applied to the dual QP problem and it was tailored for theMPC application. Results from numerical experiments indicatethat the performance of the new algorithm is very good, bothfor linear MPC as well as for hybrid MPC. It is also noticed thatthe number of QP iterations is significantly reduced compared to classical active set methods.

  • 18.
    Axehill, Daniel
    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.
    A Dual Gradient Projection Quadratic Programming Algorithm Tailored for Model Predictive Control2008In: Proceedings of the 47th IEEE Conference on Decision and Control, 2008, p. 3057-3064Conference paper (Refereed)
    Abstract [en]

    The objective of this work is to derive a QP algorithm tailored for MPC. More specifically, the primary target application is MPC for discrete-time hybrid systems. A desired property of the algorithm is that warm starts should be possible to perform efficiently. This property is very important for on-line linear MPC, and it is crucial in branch and bound for hybrid MPC. In this paper, a dual active set-like QP method was chosen because of its warm start properties. A drawback with classical active set methods is that they often require many iterations in order to find the active set in optimum. Gradient projection methods are methods known to be able to identify this active set very fast and such a method was therefore chosen in this work. The gradient projection method was applied to the dual QP problem and it was tailored for the MPC application. Results from numerical experiments indicate that the performance of the new algorithm is very good, both for linear MPC as well as for hybrid MPC. It is also noticed that the number of QP iterations is significantly reduced compared to classical active set methods. 

  • 19.
    Axehill, Daniel
    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.
    A Mixed Integer Dual Quadratic Programming Algorithm Tailored for MPC2007Report (Other academic)
    Abstract [en]

    The objective of this work is to derive an MIQP solver tailored for MPC. The MIQP solver is built on the branch and bound method, where QP relaxations of the original problem are solved in the nodes of a binary search tree. The difference between the subproblems is often small and therefore it is interesting to be able to use a previous solution as a starting point in a new subproblem. This is referred to as a warm start of the solver. Because of its good warm start properties, a dual active set QP method was chosen. The method is tailored for MPC by solving a part of the KKT system using a Riccati recursion, which makes the computational complexity of the QP iterations grow linearly with the prediction horizon. Simulation results are presented both for the QP solver itself and when it is incorporated as a part of the MIQP solver. In both cases the computational complexity is significantly reduced compared to if a primal active set solver not utilizing structure is used.

  • 20.
    Axehill, Daniel
    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.
    A Mixed Integer Dual Quadratic Programming Algorithm Tailored for MPC2006In: Proceedings of the 45th IEEE Conference on Decision and Control, 2006, p. 5693-5698Conference paper (Refereed)
    Abstract [en]

    The objective of this work is to derive an MIQP solver tailored for MPC. The MIQP solver is built on the branch and bound method, where QP relaxations of the original problem are solved in the nodes of a binary search tree. The difference between the subproblems is often small and therefore it is interesting to be able to use a previous solution as a starting point in a new subproblem. This is referred to as a warm start of the solver. Because of its good warm start properties, a dual active set QP method was chosen. The method is tailored for MPC by solving a part of the KKT system using a Riccati recursion, which makes the computational complexity of the QP iterations grow linearly with the prediction horizon. Simulation results are presented both for the QP solver itself and when it is incorporated as a part of the MIQP solver. In both cases the computational complexity is significantly reduced compared to if a primal active set solver not utilizing structure is used.

  • 21.
    Axehill, Daniel
    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.
    A Preprocessing Algorithm for MIQP Solvers with Applications to MPC2004In: Proceeding of Reglermöte 2004, 2004Conference paper (Other academic)
    Abstract [en]

    In this paper a preprocessing algorithm for unconstrained mixed integer quadratic programming problems and binary quadratic programming problems is presented. The algorithm applies to problems with certain properties, which are further described in the paper. When the algorithm is applied to a problem with these properties, the optimal value for some or all integer variables can be computed without approximations in polynomial time. The algorithm is first derived for the binary quadratic programming problem and the result is then extended to the mixed integer quadratic programming problem by transforming the latter problem into the first problem. Both mentioned quadratic programming problems have several important applications. In this paper, the focus is on model predictive control problems with both real-valued and binary control signals. As an illustration of the method, the algorithm is applied to two different problems of this type.

  • 22.
    Axehill, Daniel
    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.
    A Preprocessing Algorithm for MIQP solvers with Applications to MPC2004Report (Other academic)
    Abstract [en]

    In this paper a preprocessing algorithm for unconstrained mixed integer quadratic programming problems and binary quadratic programming problems is presented. The algorithm applies to problems with certain properties, which are further described in the paper. When the algorithm is applied to a problem with these properties, the optimal value for some or all integer variables can be computed without approximations in polynomial time. The algorithm is first derived for the binary quadratic programming problem and the result is then extended to the mixed integer quadratic programming problem by transforming the latter problem into the first problem. Both mentioned quadratic programming problems have several important applications. In this paper, the focus is on model predictive control problems with both real-valued and binary control signals. As an illustration of the method, the algorithm is applied to two different problems of this type.

  • 23.
    Axehill, Daniel
    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.
    A Preprocessing Algorithm for MIQP Solvers with Applications to MPC2004In: Proceedings of the 43rd IEEE Conference on Decision and Control, 2004, p. 2497-2502Conference paper (Refereed)
    Abstract [en]

    In this paper a preprocessing algorithm for unconstrained mixed integer quadratic programming problems and binary quadratic programming problems is presented. The algorithm applies to problems with certain properties, which are further described in the paper. When the algorithm is applied to a problem with these properties, the optimal value for some or all integer variables can be computed without approximations in polynomial time. The algorithm is first derived for the binary quadratic programming problem and the resultis then extended to the mixed integer quadratic programming problem by transforming the latter problem into the first problem. Both mentioned quadratic programming problems have several important applications. In this paper, the focus is on model predictive control problems with both real-valued and binary control signals. As an illustration of the method, the algorithm is applied to two different problems of this type.

  • 24.
    Axehill, Daniel
    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.
    Mixed Integer Predictive Control Using a Tailored Mixed Integer Dual Quadratic Programming Algorithm2006In: Proceedings of Reglermöte 2006, 2006Conference paper (Other academic)
    Abstract [en]

    The objective with this work is to derive an MIQP solver tailored for MPC. The MIQP solver is built on the branch and bound method, where QP relaxations of the original problem are solved in the nodes of a binary search tree. The difference between the subproblems is often small and therefore it is interesting to be able to use a previous solution as a starting point in a new subproblem. This is referred to as a warm start of the solver. Because of its good warm start properties, a dual active set QP method was chosen. The method is tailored for MPC by solving a part of the KKT system using a Riccati recursion, which makes the computational complexity of the QP iterations grow linearly with the prediction horizon. Simulation results are presented both for the QP solver itself and when it is incorporated as a part of the MIQP solver. In both cases the computational complexity is significantly reduced compared to if a primal active set solver not utilizing structure is used.

  • 25.
    Axehill, Daniel
    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.
    Parallel implementation of hybrid MPC2014In: Distributed Model Predictive Control Made Easy / [ed] José M. Maestre and Rudy R. Negenborn, Springer Netherlands, 2014, p. 375-392Chapter in book (Refereed)
    Abstract [en]

    In this chapter parallel implementations of hybrid MPC will be discussed. Different methods for achieving parallelism at different levels of the algorithms will be surveyed. It will be seen that there are many possible ways of obtaining parallelism for hybrid MPC, and it is by no means clear which possibilities that should be utilized to achieve the best possible performance. To answer this question is a challenge for future research.

  • 26.
    Axehill, Daniel
    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.
    Towards Parallel Implementation of Hybrid MPC: A Survey and Directions for Future Research2012In: Distributed decision making and control / [ed] Rolf Johansson and Anders Rantzer, Springer London, 2012, p. 313-338Conference paper (Refereed)
    Abstract [en]

    In this chapter parallel implementations of hybrid MPC will be discussed. Different methods for achieving parallelism at different levels of the algorithms will be surveyed. It will be seen that there are many possible ways of obtaining parallelism for hybrid MPC, and it is by no means clear which possibilities that should be utilized to achieve the best possible performance. To answer this question is a challenge for future research.

  • 27.
    Axehill, Daniel
    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.
    Gunnarsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Low-Complexity High-Performance Preprocessing Algorithm for Multiuser Detection using Gold Sequences2008Report (Other academic)
    Abstract [en]

    The optimum multiuser detection problem can be formulated as a maximum likelihood problem, which yields a binary quadratic programming problem to be solved. Generally this problem is NP-hard and is therefore hard to solve in real time. In this paper, a preprocessing algorithm is presented which makes it possible to detect some or all users optimally for a low computational cost if signature sequences with low cross correlation, e.g., Gold sequences, are used. The algorithm can be interpreted as, e.g., an adaptive tradeoff between parallel interference cancellation and successive interference cancellation. Simulations show that the preprocessing algorithm is able to optimally compute more than 94,% of the bits in the problem when the users are time-synchronous, even though the system is heavily loaded and affected by noise. Any remaining bits, not computed by the preprocessing algorithm, can either be computed by a suboptimal detector or an optimal detector. Simulations of the time-synchronous case show that if a suboptimal detector is chosen, the bit error rate (BER) rate is significantly reduced compared with using the suboptimal detector alone.

  • 28.
    Axehill, Daniel
    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.
    Gunnarsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Low-Complexity High-Performance Preprocessing Algorithm for Multiuser Detection using Gold Sequences2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 9, p. 4377-4385Article in journal (Refereed)
    Abstract [en]

    The optimum multiuser detection problem can be formulated as a maximum likelihood problem, which yields a binary quadratic programming problem to be solved. Generally this problem is NP-hard and is therefore hard to solve in real time. In this paper, a preprocessing algorithm is presented which makes it possible to detect some or all users optimally for a low computational cost if signature sequences with low cross correlation, e.g., Gold sequences, are used. The algorithm can be interpreted as, e.g., an adaptive tradeoff between parallel interference cancellation and successive interference cancellation. Simulations show that the preprocessing algorithm is able to optimally compute more than 94,% of the bits in the problem when the users are time-synchronous, even though the system is heavily loaded and affected by noise. Any remaining bits, not computed by the preprocessing algorithm, can either be computed by a suboptimal detector or an optimal detector. Simulations of the time-synchronous case show that if a suboptimal detector is chosen, the bit error rate (BER) rate is significantly reduced compared with using the suboptimal detector alone.

  • 29.
    Axehill, Daniel
    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.
    Vandenberghe, Lieven
    University of California, LA, USA.
    Relaxations Applicable to Mixed Integer Predictive Control - Comparisons and Efficient Computations2008Report (Other academic)
    Abstract [en]

    In this work, different relaxations applicable to an MPC problem with a mix of real valued and binary valued control signals are compared. In the problem description considered, there are linear inequality constraints on states and control signals. The relaxations are related theoretically and both the tightness of the bounds and the computational complexities are compared in numerical experiments. The relaxations considered are the quadratic programming (QP) relaxation, the standard semidefinite programming (SDP) relaxation and an equality constrained SDP relaxation. The result is that the standard SDP relaxation is the one that usually gives the best bound and is most computationally demanding, while the QP relaxation is the one that gives the worst bound and is least computationally demanding. The equality constrained relaxation presented in this paper often gives a better bound than the QP relaxation and is less computationally demanding compared to the standard SDP relaxation. Furthermore, it is also shown how the equality constrained SDP relaxation can be efficiently computed by solving the Newton system in an Interior Point algorithm using a Riccati recursion. This makes it possible to compute the equality constrained relaxation with approximately linear computational complexity in the prediction horizon.

  • 30.
    Axehill, Daniel
    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.
    Vandenberghe, Lieven
    University of Californa, LA, USA.
    Relaxations Applicable to Mixed Integer Predictive Control – Comparisons and Efficient Computations2007In: Proceedings of the 46th IEEE Conference on Decision and Control, 2007, p. 4103-4109Conference paper (Refereed)
    Abstract [en]

    In this work, different relaxations applicable to an MPC problem with a mix of real valued and binary valued control signals are compared. In the problem description considered, there are linear inequality constraints on states and control signals. The relaxations are related theoretically and both the tightness of the bounds and the computational complexities are compared in numerical experiments. The relaxations considered are the quadratic programming (QP) relaxation, the standard semidefinite programming (SDP) relaxation and an equality constrained SDP relaxation. The result is that the standard SDP relaxation is the one that usually gives the best bound and is most computationally demanding, while the QP relaxation is the one that gives the worst bound and is least computationally demanding. The equality constrained relaxation presented in this paper often gives a better bound than the QP relaxation and is less computationally demanding compared to the standard SDP relaxation. Furthermore, it is also shown how the equality constrained SDP relaxation can be efficiently computed by solving the Newton system in an Interior Point algorithm using a Riccati recursion. This makes it possible to compute the equality constrained relaxation with approximately linear computational complexity in the prediction horizon.

  • 31.
    Axehill, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Vandenberghe, Lieven
    University of California Los Angeles, USA.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Convex Relaxations for Mixed Integer Predictive Control2010In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 46, no 9, p. 1540-1545Article in journal (Refereed)
    Abstract [en]

    The main objective in this work is to compare different convex relaxations for Model Predictive Control (MPC) problems with mixed real valued and binary valued control signals. In the problem description considered, the objective function is quadratic, the dynamics are linear, and the inequality constraints on states and control signals are all linear. The relaxations are related theoretically and the quality of the bounds and the computational complexities are compared in numerical experiments. The investigated relaxations include the Quadratic Programming (QP) relaxation, the standard Semidefinite Programming (SDP) relaxation, and an equality constrained SDP relaxation. The equality constrained SDP relaxation appears to be new in the context of hybrid MPC and the result presented in this work indicates that it can be useful as an alternative relaxation, which is less computationally demanding than the ordinary SDP relaxation and which often gives a better bound than the bound from the QP relaxation. Furthermore, it is discussed how the result from the SDP relaxations can be used to generate suboptimal solutions to the control problem. Moreover, it is also shown that the equality constrained SDP relaxation is equivalent to a QP in an important special case.

  • 32.
    Axehill, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Vandenberghe, Lieven
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Relaxations Applicable to Model Predictive Control for Systems with Binary Control Signals2007Report (Other academic)
    Abstract [en]

    In this work, different relaxations applicable to an MPC problem with binary control signals are compared. The relaxations considered are the QP relaxation, the standard SDP relaxation and an equality constrained SDP relaxation. The relaxations are related theoretically and both the tightness of the bounds and the computational complexities are compared in numerical experiments.The result is that the standard SDP relaxation is the one that usually gives the best bound and is most computationally demanding, while the QP relaxation is the one that gives the worst bound and is least computationally demanding. The equality constrained relaxation presented in this paper often gives a better bound than the QP relaxation and is much less computationally demanding compared to the standard SDP relaxation. Furthermore, for a special case, it is shown that the equality constrained SDP relaxation can be cast in the form of a QP. This makes it possible to replace the ordinary QP relaxation usually used in branch and bound for these problems witha tighter SDP relaxation. Numerical experiments indicate that this relaxation can decrease the overall computational time spent in branch and bound.

  • 33.
    Axehill, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Vandenberghe, Lieven
    University of California, USA.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Relaxations Applicable to Model Predictive Control for Systems with Binary Control Signals2007In: Proceedings of the 7th IFAC Symposium on Nonlinear Control Systems, Curran Associates, Inc., 2007, p. 585-590Conference paper (Refereed)
    Abstract [en]

    In this work, different relaxations applicable to an MPC problem with binary control signals are compared. The relaxations considered are the QP relaxation, the standard SDP relaxation and an alternative equality constrained SDP relaxation. The relaxations are related theoretically, and both the tightness of the bounds and the computational complexities are compared in numerical experiments. The result is that for long prediction horizons, the equality constrained SDP relaxation proposed in this paper provides a good trade-off between the quality of the relaxation and the computational time.

  • 34.
    Enqvist, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    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.
    The CDIO Initiative from an Automatic Control Project Course Perspective2005In: Proceedings of the 16th IFAC World Congress, 2005, p. 2283-2283Conference paper (Refereed)
    Abstract [en]

    The CDIO (Conceive Design Implement Operate) Initiative is explained, and some of the results at the Applied Physics and Electrical Engineering program at Linköping University, Sweden, are presented. A project course in Automatic Control is used as an example. The projects within the course are carried out using the LIPS (Linköping interactive project steering) model. An example of a project, the golf playing industrial robot, and the results from this project are also covered.

  • 35.
    Enqvist, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    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.
    The CDIO Initiative from an Automatic Control Project Course Perspective2004Report (Other academic)
    Abstract [en]

    The CDIO (Conceive Design Implement Operate) Initiative is explained, and some of the results at the Applied Physics and Electrical Engineering program at Linköping University, Sweden, are presented. A project course in Automatic Control is used as an example. The projects within the course are carried out using the LIPS (Linköping interactive project steering) model. An example of a project, the golf playing industrial robot, and the results from this project are also covered.

  • 36.
    Falkeborg, Rikard
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Löfberg, Johan
    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.
    Low-Rank Exploitation in Semidefinite Programming for Control2010In: Proceedings of Reglermöte 2010, 2010Conference paper (Other academic)
    Abstract [en]

    Many control related problems can be cast as semidefinite programs but, even though there exist polynomial time algorithms and good publicly available solvers, the time it takes to solve these problems can be long. Something many of these problems have in common, is that some of the variables enter as matrix valued variables. This leads to a low-rank structure in the basis matrices which can be exploited when forming the Newton equations. In this paper, we describe how this can be done, and show how our code can be used when using SDPT3. The idea behind this is old and is implemented in LMI Lab, but we show that when using a modern algorithm, the computational time can be reduced. Finally, we describe how the modeling language YALMIP is changed in such a way that our code can be interfaced using standard YALMIP commands, which greatly simplifies for the user.

  • 37.
    Falkeborn, Rikard
    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.
    A Decomposition Algorithm for KYP-SDPs2009In: Proceedings of the 10th European Control Conference (ECC), 2009, p. 3202-3207Conference paper (Refereed)
    Abstract [en]

    In this paper, a structure exploiting algorithm for semidefinite programs derived from the Kalman-Yakubovich-Popov lemma, where some of the constraints appear as complicating constraints is presented. A decomposition algorithm is proposed, where the structure of the problem can be utilized. In a numerical example, where a controller that minimizes the sum of the H2-norm and the H-norm is designed, the algorithm is shown to be faster than SeDuMi and the special purpose solver KYPD.

  • 38.
    Falkeborn, Rikard
    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.
    A Decomposition Algorithm for KYP-SDPs2009Report (Other academic)
    Abstract [en]

    In this paper, a structure exploiting algorithm for semidefinite programs derived from the Kalman-Yakubovich-Popov lemma, where some of the constraints appear as complicating constraints is presented. A decomposition algorithm is proposed, where the structure of the problem can be utilized. In a numerical example, where a controller that minimizes the sum of the H2-norm and the H-norm is designed, the algorithm is shown to be faster than SeDuMi and the special purpose solver KYPD.

  • 39.
    Falkeborn, Rikard
    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.
    A Decomposition Algorithm for KYP-SDPs2012In: European Journal of Control, ISSN 0947-3580, E-ISSN 1435-5671, Vol. 18, no 3, p. 249-256Article in journal (Refereed)
    Abstract [en]

    In this paper, a structure exploiting algorithm for semidefinite programs derived from the Kalmatz-Yakubovich-Popov lemma, where some of the constraints appear as complicating constraints is presented. A decomposition algorithm is proposed, where the structure of the problem can be utilized. In a numerical example, where a controller that minimizes the stun of the H-2-norm and the H-infinity-norm is designed, the algorithm, is shown to be faster than SeDuMi and the special purpose solver KYPD.

  • 40.
    Falkeborn, Rikard
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Löfberg, Johan
    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.
    Low-Rank Exploitation in Semidefinite Programming for Control2010In: Proceedings of the 2010 IEEE International Symposium on Computer-Aided Control System Design, 2010, p. 24-28Conference paper (Refereed)
    Abstract [en]

    Many control related problems can be cast as semidefinite programs but, even though there exist polynomial time algorithms and good publicly available solvers, the time it takes to solve these problems can be long. Something many of these problems have in common, is that some of the variables enter as matrix valued variables. This leads to a low-rank structure in the basis matrices which can be exploited when forming the Newton equations. In this paper, we describe how this can be done, and show how our code can be used when using SDPT3. The idea behind this is old and is implemented in LMI Lab, but we show that when using a modern algorithm, the computational time can be reduced. Finally, we describe how the modeling language YALMIP is changed in such a way that our code can be interfaced using standard YALMIP commands, which greatly simplifies for the user.

  • 41.
    Falkeborn, Rikard
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Löfberg, Johan
    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.
    Low-Rank Exploitation in Semidefinite Programming for Control2010Report (Other academic)
    Abstract [en]

    Many control related problems can be cast as semidefinite programs but, even though there exist polynomial time algorithms and good publicly available solvers, the time it takes to solve these problems can be long. Something many of these problems have in common, is that some of the variables enter as matrix valued variables. This leads to a low-rank structure in the basis matrices which can be exploited when forming the Newton equations. In this paper, we describe how this can be done, and show how our code can be used when using SDPT3. The idea behind this is old and is implemented in LMI Lab, but we show that when using a modern algorithm, the computational time can be reduced. Finally, we describe how the modeling language YALMIP is changed in such a way that our code can be interfaced using standard YALMIP commands, which greatly simplifies for the user.

  • 42.
    Falkeborn, Rikard
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Löfberg, Johan
    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.
    Low-Rank Exploitation in Semidefinite Programming for Control2011In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 84, no 12, p. 1975-1982Article in journal (Refereed)
    Abstract [en]

    Many control-related problems can be cast as semidefinite programs. Even though there exist polynomial time algorithms and excellent publicly available solvers, the time it takes to solve these problems can be excessive. What many of these problems have in common, in particular in control, is that some of the variables enter as matrix-valued variables. This leads to a low-rank structure in the basis matrices which can be exploited when forming the Newton equations. In this article, we describe how this can be done, and show how our code, called STRUL, can be used in conjunction with the semidefinite programming solver SDPT3. The idea behind the structure exploitation is classical and is implemented in LMI Lab, but we show that when using a modern semidefinite programming framework such as SDPT3, the computational time can be significantly reduced. Finally, we describe how the modelling language YALMIP has been changed in such a way that our code, which can be freely downloaded, can be interfaced using standard YALMIP commands. This greatly simplifies modelling and usage.

  • 43.
    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.

  • 44.
    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.

  • 45.
    Gillberg, Jonas
    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.
    Polynomial Complexity for a Nesterov-Todd Potential-Reduction Method with Inexact Search Directions: Examples related to the KYP Lemma2003Report (Other academic)
    Abstract [en]

    In this paper is discussed how to efficiently solve semidefinite programs related to the Kalman-Yakubovich-Popov lemma. We consider a potential-reduction method where Nesterov-Todd search directions are computed inexactly by applying a preconditioned conjugate gradient method on the Schur complement equations. An efficient preconditioner based on Lyapunov equations is derived. We give a proof of polynomial convergence for this interior point method.

  • 46.
    Hansson, Anders
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gillberg, Jonas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Polynomial Complexity for a Nesterov-Todd Potential-Reduction Method with Inexact Search Directions2003In: Proceedings of the 42nd IEEE Conference on Decision and Control, 2003, p. 3824-3829 vol.4Conference paper (Refereed)
    Abstract [en]

    In this paper is discussed how to efficiently solve semidefinite programs related to the Kalman-Yakubovich-Popov lemma. We consider a potential-reduction method where Nesterov-Todd search directions are computed inexactly by applying a preconditioned conjugate gradient method to the Schur complement equation. An efficient preconditioner based on Lyapunov equations is derived. We give a proof of polynomial convergence for this interior point method.

  • 47.
    Hansson, Anders
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Glad, Torkel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Stability Analysis of Nonlinear Systems using Frozen Stationary Linearization2004In: Proceedings of the 2004 American Control Conference, 2004, p. 424-428Conference paper (Refereed)
    Abstract [en]

    This work discusses how to compute stability regions for nonlinear systems with slowly varying parameters using frozen stationary linearization. It is shown that larger stability regions can be obtained as compared to traditional approaches using recent stability results for linear parameter-varying systems.

  • 48.
    Hansson, Anders
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Helmersson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Glad, Torkel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Stability Analysis of Nonlinear Systems using Frozen Stationary Linearization2003Report (Other academic)
    Abstract [en]

    This work discusses how to compute stability regions for nonlinear systems with slowly varying parameters using frozen stationary linearization. It is shown that larger stability regions can be obtained as compared to traditional approaches using recent stability results for linear parameter-varying systems.

  • 49.
    Hansson, Anders
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Jansson, Henrik
    Royal Institute of Technology, Sweden.
    Hjalmarsson, Håkan
    Royal Institute of Technology, Sweden.
    On Methods for Gradient Estimation in IFT for MIMO Systems2002In: Proceedings of 15th IFAC World Congress, 2002, p. 1047-1047Conference paper (Refereed)
    Abstract [en]

    Iterative feedback tuning (IFT) is a model free control tuning method using closed loop experiments. For single-input single-output (SISO) systems only 2 or 3, depending on the controller structure, closed loop experiments are required. However for multivariable systems the number of experiments increase to a maximum of 1 + m times p, where m times p is the dimension of the controller. In this contribution several methods are proposed to reduce the experimental time by approximating the gradient of the cost function. The local convergence for a method which uses the same technique as in IFT for SISO systems is analyzed. It is shown that even if there are commutation errors due to the approximation method, the numerical optimization may still converge to the true optimum.

  • 50.
    Hansson, Anders
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Liu, Zhang
    Northrop Grumman Corporation, San Diego, USA.
    Vandenberghe, Lieven
    UCLA, Los Angels, USA.
    Subspace System Identification via Weighted Nuclear Norm Optimization in 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), vol , issue , pp 3439-34442012Conference paper (Refereed)
    Abstract [en]

    We present a subspace system identification method based on weighted nuclear norm approximation. The weight matrices used in the nuclear norm minimization are the same weights as used in standard subspace identification methods. We show that the inclusion of the weights improves the performance in terms of fit on validation data. Experimental results from randomly generated examples as well as from the Daisy benchmark collection are reported. The key to an efficient implementation is the use of the alternating direction method of multipliers to solve the optimization problem.

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