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Gainscheduling control with dynamic multipliers by convex optimization
 SIAM Journal on Matrix Analysis and Applications (preprint
"... Abstract. In this paper we provide a solution to the gainscheduling synthesis problem (GSP) for structured parametric and dynamic passive uncertainties, which encompasses an approach for normbounded uncertainties as considered in classical structured singular value theory. If compared to known app ..."
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Cited by 1 (1 self)
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controllers and discuss the benefits of this new design methodology. Key words. gainscheduled control, dynamic generalized positive real multipliers, D/Gscalings, linear matrix inequalities
Convex Analysis
, 1970
"... In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a lo ..."
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Cited by 5350 (67 self)
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In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a
GainScheduled
"... This paper is concerned with the application of advanced Linear ParameterVarying (LPV) techniques to the global control of a missile. The LPV technique considered in this paper is an extension of the standard H1 synthesis technique to the case where the plant depends affinely on a timevarying vect ..."
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varying vector `(t). Working in the class of LPV plants, the proposed methodology produces an LPV controller. That is, a controller which is automatically "gainscheduled" along the trajectories of the plant. LPV controllers solutions to the problem are characterized via a set of Riccati Linear Matrix
A Convex Characterization of GainScheduled Hâˆž Controllers
"... An important class of linear timevarying systems consists of plants where the statespace matrices are fixed functions of some timevarying physical parameters `. Small Gain techniques can be applied to such systems to derive robust timeinvariant controllers. Yet, this approach is often overly ..."
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Cited by 102 (5 self)
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âˆž synthesis techniques to allow for controller dependence on timevarying but measured parameters. When this dependence is linear fractional, the existence of such gainscheduled H1 controllers is fully characterized in terms of linear matrix inequalities (LMIs). The underlying synthesis problem
Optimization Flow Control, I: Basic Algorithm and Convergence
 IEEE/ACM TRANSACTIONS ON NETWORKING
, 1999
"... We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm. In thi ..."
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Cited by 690 (64 self)
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We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm
GainScheduled Control of a
"... In this paper the objective is to optimize the control of a coal fired 250 MW power plant boiler. The conventional control system is supplemented with a multivariable optimizing controller operating in parallel with the conventional control system. Due to the strong dependence of the gains and d ..."
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and dynamics upon the load, it is beneficial to consider a gainscheduling control approach. Optimization using complexp synthesis results in unstahle LTI controllers in some operating points of the boiler. A recent gainscheduling approach allowing for unstable fixed LTI controllers is applied. Gainscheduling
Constrained model predictive control: Stability and optimality
 AUTOMATICA
, 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
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Cited by 696 (15 self)
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Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 496 (2 self)
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. This paper studies a method called convex relaxation, which attempts to recover the ideal sparse signal by solving a convex program. This approach is powerful because the optimization can be completed in polynomial time with standard scientific software. The paper provides general conditions which ensure
Particle swarm optimization
, 1995
"... eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications ..."
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Cited by 3535 (22 self)
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eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 582 (23 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
Results 1  10
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468,175