Results 1  10
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65
Robust Monetary Policy under Model Uncertainty in a Small Model of the US Economy. Forthcoming in Macroeconomic Dynamics
, 1999
"... One of the prominent ways to analyze the robustness of monetary policy under model uncertainty consists of the following three steps. First, choose a reference model of the economy. Next, define a set of perturbations around this model, where the set is structured so that the uncertainty is focused ..."
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Cited by 79 (3 self)
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One of the prominent ways to analyze the robustness of monetary policy under model uncertainty consists of the following three steps. First, choose a reference model of the economy. Next, define a set of perturbations around this model, where the set is structured so that the uncertainty is focused on potentially important weaknesses of the reference model. Finally, choose policy so that it works best for the worst model from the set. Previous applications of this approach allowed only for purely backwardlooking models. This paper extends the analysis of robustness to models that may include forwardlooking components. Empirical part of the paper studies simple policy rules under model, data and shock uncertainty in a small model of the US economy with rational expectations. Key words: structured uncertainty, rational expectations, robustness.
Enhancing Sparsity by Reweighted ℓ1 Minimization
, 2007
"... It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many si ..."
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Cited by 76 (5 self)
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It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many situations outperforms ℓ1 minimization in the sense that substantially fewer measurements are needed for exact recovery. The algorithm consists of solving a sequence of weighted ℓ1minimization problems where the weights used for the next iteration are computed from the value of the current solution. We present a series of experiments demonstrating the remarkable performance and broad applicability of this algorithm in the areas of sparse signal recovery, statistical estimation, error correction and image processing. Interestingly, superior gains are also achieved when our method is applied to recover signals with assumed nearsparsity in overcomplete representations—not by reweighting the ℓ1 norm of the coefficient sequence as is common, but by reweighting the ℓ1 norm of the transformed object. An immediate consequence is the possibility of highly efficient data acquisition protocols by improving on a technique known as compressed sensing.
Multiobjective output feedback control via LMI
 in Proc. Amer. Contr. Conf
, 1997
"... The problem of multiobjective H2=H1 optimal controller design is reviewed. There is as yet no exact solution to this problem. We present a method based on that proposed by Scherer [14]. The problem is formulated as a convex semidefinite program (SDP) using the LMI formulation of the H2 and H1 norms. ..."
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Cited by 72 (5 self)
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The problem of multiobjective H2=H1 optimal controller design is reviewed. There is as yet no exact solution to this problem. We present a method based on that proposed by Scherer [14]. The problem is formulated as a convex semidefinite program (SDP) using the LMI formulation of the H2 and H1 norms. Suboptimal solutions are computed using finite dimensional Qparametrization. The objective value of the suboptimal Q's converges to the true optimum as the dimension of Q is increased. State space representations are presented which are the analog of those given by Khargonekar and Rotea [11] for the H2 case. A simple example computed using FIR (Finite Impulse Response) Q's is presented.
Enhacing sparsity by reweighted ℓ1 minimization
 Journal of Fourier Analysis and Applications
, 2008
"... It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many si ..."
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Cited by 34 (1 self)
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It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many situations outperforms ℓ1 minimization in the sense that substantially fewer measurements are needed for exact recovery. The algorithm consists of solving a sequence of weighted ℓ1minimization problems where the weights used for the next iteration are computed from the value of the current solution. We present a series of experiments demonstrating the remarkable performance and broad applicability of this algorithm in the areas of sparse signal recovery, statistical estimation, error correction and image processing. Interestingly, superior gains are also achieved when our method is applied to recover signals with assumed nearsparsity in overcomplete representations—not by reweighting the ℓ1 norm of the coefficient sequence as is common, but by reweighting the ℓ1 norm of the transformed object. An immediate consequence is the possibility of highly efficient data acquisition protocols by improving on a technique known as compressed sensing.
Distributed control of spatially invariant systems
 IEEE Transactions on Automatic Control
, 2002
"... Abstract—We consider distributed parameter systems where the underlying dynamics are spatially invariant, and where the controls and measurements are spatially distributed. These systems arise in many applications such as the control of vehicular platoons, flow control, microelectromechanical system ..."
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Cited by 33 (0 self)
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Abstract—We consider distributed parameter systems where the underlying dynamics are spatially invariant, and where the controls and measurements are spatially distributed. These systems arise in many applications such as the control of vehicular platoons, flow control, microelectromechanical systems (MEMS), smart structures, and systems described by partial differential equations with constant coefficients and distributed controls and measurements. For fully actuated distributed control problems involving quadratic criteria such as linear quadratic regulator (LQR), P and, optimal controllers can be obtained by solving a parameterized family of standard finitedimensional problems. We show that optimal controllers have an inherent degree of decentralization, and this provides a practical distributed controller architecture. We also prove a general result that applies to partially distributed control and a variety of performance criteria, stating that optimal controllers inherit the spatial invariance structure of the plant. Connections of this work to that on systems over rings, and systems with dynamical symmetries are discussed. Index Terms—Distributed control, infinitedimensional systems, optimal control, robust control, spatially invariant systems.
Disciplined convex programming
 Global Optimization: From Theory to Implementation, Nonconvex Optimization and Its Application Series
, 2006
"... ..."
Setvalued observers and optimal disturbance rejection
 IEEE Transactions on Automatic Control
, 1999
"... Abstract—A setvalued observer (also called guaranteed state estimator) produces a set of possible states based on output measurements and models of exogenous signals. In this paper, we consider the guaranteed state estimation problem for linear timevarying systems with a priori magnitude bounds on ..."
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Cited by 20 (5 self)
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Abstract—A setvalued observer (also called guaranteed state estimator) produces a set of possible states based on output measurements and models of exogenous signals. In this paper, we consider the guaranteed state estimation problem for linear timevarying systems with a priori magnitude bounds on exogenous signals. We provide an algorithm to propagate the set of possible states based on output measurements and show that the centers of these sets provide optimal estimates in an `1induced norm sense. We then consider the utility of setvalued observers for disturbance rejection with output feedback and derive the following general separation structure. An optimal controller can consist of a setvalued observer followed by a static nonlinear function on the observed set of possible states. A general construction of this function is provided in the scalar control case. Furthermore, in the special case of fullcontrol, i.e., the number of control inputs equals the number of states, optimal output feedback controllers can take the form of an optimal estimate of the fullstate feedback controller. Index Terms — Disturbance rejection, state estimation, observers. I.
Limited Authority Adaptive Flight Control
, 2000
"... Contents Acknowledgements iii List of Figures vii Nomenclature xi Summary xiv 1 Introduction 1 1.1 Adaptive Flight Control for Reusable Launch Vehicles .......................................1 1.2 Design Integration Problems in Adaptive Control ................................................5 1.2.1 ..."
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Cited by 15 (9 self)
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Contents Acknowledgements iii List of Figures vii Nomenclature xi Summary xiv 1 Introduction 1 1.1 Adaptive Flight Control for Reusable Launch Vehicles .......................................1 1.2 Design Integration Problems in Adaptive Control ................................................5 1.2.1 Saturation ..................................................................... ..............................5 1.2.2 Linear Input Dynamics............................................................. ..................9 1.2.3 Quantized Control ..................................................................... ...............10 1.2.4 Adaptation While Not in Direct Control ..................................................10 1.2.5 Flight Certification of Adaptive Controllers.............................................11 1.3 Contributions of This Research ..................................................................... .....12 1.4 Brief Outline of Thesis ...............
A New Controller Architecture for High Performance, Robust, and Fault Tolerant Control
 IEEE Transactions on Automatic Control
, 2001
"... In this paper, we propose a new feedback controller architecture. The distinguished feature of our new controller architecture is that the controller design for performance and robustness can be done separately which completely overcome the conflict between performance and robustness in the traditio ..."
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Cited by 14 (0 self)
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In this paper, we propose a new feedback controller architecture. The distinguished feature of our new controller architecture is that the controller design for performance and robustness can be done separately which completely overcome the conflict between performance and robustness in the traditional feedback framework. The controller architecture includes two parts: one part for performance and the other part for robustness. The controller architecture works in suchaway that the feedback control system will be solely controlled by the performance controller when there is no model uncertainties and external disturbances and the robustification controller will only be active when there is model uncertainties or external disturbances. 1 Introduction A fundamental reason for using feedbackcontrol is to achieve desired performance in the presence of external disturbances and model uncertainties. It is well known that there is an intrinsic conflict between performance and robustness in ...