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Automatic Control, IEEE Transactions on

Issue 10 • Date Oct. 2005

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Displaying Results 1 - 24 of 24
  • Table of contents

    Publication Year: 2005 , Page(s): c1
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  • IEEE Transactions on Automatic Control publication information

    Publication Year: 2005 , Page(s): c2
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  • Guest Editorial: Special Issue on System Identification

    Publication Year: 2005 , Page(s): 1473
    Cited by:  Papers (12)
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  • Scanning the issue

    Publication Year: 2005 , Page(s): 1474 - 1476
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  • On sampled-data models for nonlinear systems

    Publication Year: 2005 , Page(s): 1477 - 1489
    Cited by:  Papers (36)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (448 KB) |  | HTML iconHTML  

    Models for deterministic continuous-time nonlinear systems typically take the form of ordinary differential equations. To utilize these models in practice invariably requires discretization. In this paper, we show how an approximate sampled-data model can be obtained for deterministic nonlinear systems such that the local truncation error between the output of this model and the true system is of ... View full abstract»

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  • Application of structured total least squares for system identification and model reduction

    Publication Year: 2005 , Page(s): 1490 - 1500
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (584 KB) |  | HTML iconHTML  

    The following identification problem is considered: Minimize the ℓ2 norm of the difference between a given time series and an approximating one under the constraint that the approximating time series is a trajectory of a linear time invariant system of a fixed complexity. The complexity is measured by the input dimension and the maximum lag. The question leads to a problem that is... View full abstract»

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  • Control-oriented model validation and errors quantification in the ℓ1 setup

    Publication Year: 2005 , Page(s): 1501 - 1508
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (232 KB) |  | HTML iconHTML  

    A priori information required for robust synthesis includes a nominal model and a model of uncertainty. The latter is typically in the form of additive exogenous disturbance and plant perturbations with assumed bounds. If these bounds are unknown or too conservative, they have to be estimated from measurement data. In this paper, the problem of errors quantification is considered in the framework ... View full abstract»

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  • Subspace identification of Hammerstein systems using least squares support vector machines

    Publication Year: 2005 , Page(s): 1509 - 1519
    Cited by:  Papers (57)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (488 KB) |  | HTML iconHTML  

    This paper presents a method for the identification of multiple-input-multiple-output (MIMO) Hammerstein systems for the goal of prediction. The method extends the numerical algorithms for subspace state space system identification (N4SID), mainly by rewriting the oblique projection in the N4SID algorithm as a set of componentwise least squares support vector machines (LS-SVMs) regression problems... View full abstract»

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  • A Bayesian approach to identification of hybrid systems

    Publication Year: 2005 , Page(s): 1520 - 1533
    Cited by:  Papers (48)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (784 KB) |  | HTML iconHTML  

    In this paper, we present a novel procedure for the identification of hybrid systems in the class of piecewise ARX systems. The presented method facilitates the use of available a priori knowledge on the system to be identified, but can also be used as a black-box method. We treat the unknown parameters as random variables, described by their probability density functions. The identification probl... View full abstract»

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  • Input design via LMIs admitting frequency-wise model specifications in confidence regions

    Publication Year: 2005 , Page(s): 1534 - 1549
    Cited by:  Papers (66)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (704 KB) |  | HTML iconHTML  

    A framework for reformulating input design problems in prediction error identification as convex optimization problems is presented. For linear time-invariant single input/single output systems, this framework unifies and extends existing results on open-loop input design that are based on the finite dimensional asymptotic covariance matrix of the parameter estimates. Basic methods for parametrizi... View full abstract»

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  • A convex analytic approach to system identification

    Publication Year: 2005 , Page(s): 1550 - 1566
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (472 KB) |  | HTML iconHTML  

    This paper introduces a new concept for system identification in order to account for random and nonrandom(deterministic/set-membership) uncertainties. While, random/stochastic models are natural for modeling measurement errors, nonrandom uncertainties are well-suited for modeling parametric and nonparametric components. The new concept introduced is distinct from earlier concepts in many respects... View full abstract»

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  • A bounded-error approach to piecewise affine system identification

    Publication Year: 2005 , Page(s): 1567 - 1580
    Cited by:  Papers (73)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (736 KB) |  | HTML iconHTML  

    This paper proposes a three-stage procedure for parametric identification of piecewise affine autoregressive exogenous (PWARX) models. The first stage simultaneously classifies the data points and estimates the number of submodels and the corresponding parameters by solving the partition into a minimum number of feasible subsystems (MIN PFS) problem for a suitable set of linear complementary inequ... View full abstract»

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  • Maximum-likelihood parameter estimation of bilinear systems

    Publication Year: 2005 , Page(s): 1581 - 1596
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (656 KB) |  | HTML iconHTML  

    This paper addresses the problem of estimating the parameters in a multivariable bilinear model on the basis of observed input-output data. The main contribution is to develop, analyze, and empirically study new techniques for computing a maximum-likelihood based solution. In particular, the emphasis here is on developing practical methods that are illustrated to be numerically reliable, robust to... View full abstract»

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  • On the role of prefiltering in nonlinear system identification

    Publication Year: 2005 , Page(s): 1597 - 1602
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (304 KB) |  | HTML iconHTML  

    Data prefiltering is often used in linear system identification to increase model accuracy in a specified frequency band, as prefiltering is equivalent to a frequency weighting on the prediction error function. However, this interpretation applies only to a strictly linear setting of the identification problem. In this note, the role of data and error prefiltering in nonlinear system identificatio... View full abstract»

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  • Kernel based partially linear models and nonlinear identification

    Publication Year: 2005 , Page(s): 1602 - 1606
    Cited by:  Papers (23)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (248 KB) |  | HTML iconHTML  

    In this note, we propose partially linear models with least squares support vector machines (LS-SVMs) for nonlinear ARX models. We illustrate how full black-box models can be improved when prior information about model structure is available. A real-life example, based on the Silverbox benchmark data, shows significant improvements in the generalization ability of the structured model with respect... View full abstract»

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  • Model quality in identification of nonlinear systems

    Publication Year: 2005 , Page(s): 1606 - 1611
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (336 KB) |  | HTML iconHTML  

    In this note, the problem of the quality of identified models of nonlinear systems, measured by the errors in simulating the system behavior for future inputs, is investigated. Models identified by classical methods minimizing the prediction error, do not necessary give "small" simulation error on future inputs and even boundedness of this error is not guaranteed. In order to investigate the simul... View full abstract»

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  • Strong consistency of recursive identification for Hammerstein systems with discontinuous piecewise-linear memoryless block

    Publication Year: 2005 , Page(s): 1612 - 1617
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (248 KB) |  | HTML iconHTML  

    This note deals with identification of Hammerstein systems with discontinuous piecewise-linear memoryless block followed by a linear subsystem. Recursive algorithms are proposed for estimating coefficients of the linear subsystem and six unknown parameters contained in the nonlinear static block. By taking a sequence of iid random variables with uniform distribution to serve as the system input, s... View full abstract»

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  • Identification of IIR Wiener systems with spline nonlinearities that have variable knots

    Publication Year: 2005 , Page(s): 1617 - 1622
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (232 KB) |  | HTML iconHTML  

    An algorithm is developed for the identification of Wiener systems, linear dynamic elements followed by static nonlinearities. In this case, the linear element is modeled using a recursive digital filter, while the static nonlinearity is represented by a spline of arbitrary but fixed degree. The primary contribution in this note is the use of variable knot splines, which allow for the use of splin... View full abstract»

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  • A nonlinear least-squares approach for identification of the induction motor parameters

    Publication Year: 2005 , Page(s): 1622 - 1628
    Cited by:  Papers (32)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (280 KB) |  | HTML iconHTML  

    A nonlinear least-squares method is presented for the identification of the induction motor parameters. A major difficulty with the induction motor is that the rotor state variables are not available measurements so that the system identification model cannot be made linear in the parameters without overparametrizing the model. Previous work in the literature has avoided this issue by making simpl... View full abstract»

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  • Subspace based approaches for Wiener system identification

    Publication Year: 2005 , Page(s): 1629 - 1634
    Cited by:  Papers (13)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (312 KB) |  | HTML iconHTML  

    We consider the problem of Wiener system identification in this note. A Wiener system consists of a linear time invariant block followed by a memoryless nonlinearity. By modeling the inverse of the memoryless nonlinearity as a linear combination of known nonlinear basis functions, we develop two subspace based approaches, namely an alternating projection algorithm and a minimum norm method, to sol... View full abstract»

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  • Put your technology leadership in writing

    Publication Year: 2005 , Page(s): 1635
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  • Celebrating the vitality of technology the Proceedings of the IEEE [advertisement]

    Publication Year: 2005 , Page(s): 1636
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  • IEEE Control Systems Society Information

    Publication Year: 2005 , Page(s): c3
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  • Blank page [back cover]

    Publication Year: 2005 , Page(s): c4
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Aims & Scope

In the IEEE Transactions on Automatic Control, the IEEE Control Systems Society publishes high-quality papers on the theory, design, and applications of control engineering.  Two types of contributions are regularly considered

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
P. J. Antsaklis
Dept. Electrical Engineering
University of Notre Dame