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Automatic Control, IEEE Transactions on

Issue 6 • Date December 1974

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Displaying Results 1 - 25 of 46
  • [Front cover and table of contents]

    Page(s): 0
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    Freely Available from IEEE
  • Introduction

    Page(s): 638 - 640
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    Freely Available from IEEE
  • [Back cover]

    Page(s): 0
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    Freely Available from IEEE
  • Stochastic theory of minimal realization

    Page(s): 667 - 674
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    In this paper it is shown that a natural representation of a state space is given by the predictor space, the linear space spanned by the predictors when the system is driven by a Gaussian white noise input with unit covariance matrix. A minimal realization corresponds to a selection of a basis of this predictor space. Based on this interpretation, a unifying view of hitherto proposed algorithmically defined minimal realizations is developed. A natural minimal partial realization is also obtained with the aid of this interpretation. View full abstract»

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  • Canonical forms for the identification of multivariable linear systems

    Page(s): 646 - 656
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    The advantage of using a unique parameterization in a numerical procedure for the identification of a system from operating records has been well established. In this paper several sets of canonical forms are described for state space models of deterministic multivariable linear systems; the members of these sets having therefore the required uniqueness property within the equivalence classes of minimal realizations of the system. In the identification of a stochastic system, it is shown how the problem depends also upon determining a unique factorization of the spectral density matrix of the system, and the sets of canonical forms obtained for the deterministic system are extended to this case. View full abstract»

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  • Stable adaptive schemes for state estimation and identification of linear systems

    Page(s): 841 - 847
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    The main contribution of this paper is the introduction of a new canonical form for an adaptive observer for a linear multivariable system. The adaptive observer simultaneously estimates the state and the parameters of the unknown plant and is shown to be globally asymptotically stable. The extension of the results to discrete systems and the application of the adaptive observer in the control of helicopter dynamics is also considered in detail. View full abstract»

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  • Least squares estimates of structural system parameters using covariance function data

    Page(s): 898 - 903
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    A statistically efficient and computationally economical two-stage least squares procedure for the estimation of the natural frequencies and damping parameters of structural systems under stationary random vibration conditions is considered. The structural system is represented by the system of ordinary differential equations that is characteristic of lumped mass-spring-damper systems with a random forcing function. Emphasis is placed on the problem corresponding to the observation of the top story vibrations of a tall building under random wind excitation. In that case, the random excitation can be approximated by a white noise and the regularly sampled vibration record can be represented as a mixed autoregressive-moving average (ARMA) time series. The ARMA time series parameters are estimated by a two-stage least squares method using only the covariance function of the top story vibrations. The natural frequency and damping parameters of the structural system can be expressed in terms of the AR parameters. Estimates of the coefficient of variation of the structural system parameter estimates are expressed in terms of the ARMA parameter estimates. The numerical results of the least squares and maximum likelihood parameter estimation procedures worked on a real vibration data example are shown. View full abstract»

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  • Identification of human driver models in car following

    Page(s): 911 - 915
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    This paper presents the development of two new mathematical models of driver behavior in single-lane car following situations. Optimum parameter values in each of these models were obtained using standard parameter identification algorithms. The basic approach to model development was to derive the model structure using optimal control theory. The problem was formulated as a model-tracking problem and a quadratic cost function was minimized. Model parameters were optimized by comparing model behavior with freeway data obtained on Interstate 71 in Ohio by Clear and Treiterer. The results indicate that the models fit the data very well during acceleration and deceleration phases, but not during constant velocity regions. To obtain a better fit during transitions between acceleration and deceleration phases, a second model was postulated based on the hypothesis that the driver tracks not only the car directly in front of him, but also cars directly ahead of the lead car. An adaptive controller structure with a mode switching algorithm has been derived and its parameters optimized. View full abstract»

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  • Linear time dependent systems

    Page(s): 735 - 737
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    In studies of linear open-loop systems, the assumption of time invariance is often tacitly made. In this paper we show how a time dependent system can arise quite naturally. The estimation of time dependent transfer function, on the basis of a single realization, when the input/output processes are nonstationary is considered. We also consider the problem of testing a given open-loop system for time dependence. The tests described here make use of the concept of the "evolutionary cross spectra," and rests essentially on testing the "uniformity" of a set of vectors, whose components consist of the "evolutionary gain spectra" and "evolutionary phase spectra." Using a logarithmic transformation on the evolutionary gain spectra, we show that the mechanics of the tests are formally equivalent to a two-factor multivariate analysis of variance (MANOVA) procedure. Numerical illustrations, from the real and simulated data, of the proposed tests are included. View full abstract»

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  • Macroeconomic modeling for control

    Page(s): 862 - 873
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    This paper describes briefly the modeling of the United Kingdom economy in the least complex structural form allowable for the application of modern control techniques. The method employed is a blend of the "black box" approach where no knowledge of the inner mechanism is assumed and the classical approach of econometrics where economic theory is used to determine structure. The technique is illustrated in detail by means of a numerical example consisting of a four-equation sector of a larger model in which a full treatment of error is given. This serves to reveal to engineers the difficulties of econometrics, demonstrating the many pit-falls and acute problems that prevent a straightforward application of the methods of control theory. View full abstract»

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  • Stochastic approximation methods for identification and control--A survey

    Page(s): 798 - 809
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    Stochastic search techniques have been the essential part for most identification and self-organizing or learning control algorithms for stochastic systems. Stochastic approximation search algorithms have been very popular among the researchers in these areas because of their simplicity of implementation, convergence properties, as well as intuitive appeal to the investigator. This paper presents an exposition of the stochastic approximation algorithms and their application to various parameter identification and self-organizing control algorithms. View full abstract»

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  • Identification of stochastic electric load models from physical data

    Page(s): 887 - 893
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    The three step identification process of model development, parameter estimation, and performance analysis is illustrated through the identification of models for the prediction of electric power demand. Each step is carefully supported by numerical results based on physical data. Three types of progressively more complex but more accurate load models are identified which describe 1) time periodicity, 2) time periodicity plus load autocorrelation, and 3) time periodicity plus load autocorrelation plus dynamic temperature effects. Accurate predictions up to one week are demonstrated. General guidelines are extrapolated from this identification example when possible. View full abstract»

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  • Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations

    Page(s): 774 - 783
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    This paper discusses numerical aspects of computing maximum likelihood estimates for linear dynamical systems in state-vector form. Different gradient-based nonlinear programming methods are discussed in a unified framework and their applicability to maximum likelihood estimation is examined. The problems due to singular Hessian or singular information matrix that are common in practice are discussed in detail and methods for their solution are proposed. New results on the calculation of state sensitivity functions via reduced order models are given. Several methods for speeding convergence and reducing computation time are also discussed. View full abstract»

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  • Time series analysis

    Page(s): 706 - 715
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    This paper begins with a discussion of the properties of the noise occurring in the structures considered. This noise is taken to be generated by a stationary, ergodic, purely nondeterministic process. In case the observed vector sequence is generated by an autoregressive-moving average process then (a little more than) the additional requirement that the best predictor be the best linear predictor suffices for the development of an asymptotic inference theory. Signal measurement problems are considered, first where the signal is directly observed except for some unknown parameters and second where the signal is not directly observable and some characteristics, such as the velocity of propagation, have to be measured. Finally nonstationary models, nonlinear models for prediction, transient signals, and irregularly spaced samples are briefly discussed. Throughout, the methods are based on the use of the fast Fourier transforms of the data and their relation to the use of quasimaximum likelihoods in terms of those transforms is discussed. View full abstract»

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  • Identification and autoregressive spectrum estimation

    Page(s): 894 - 898
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    In recent years there has been increasing interest in autoregressive spectrum estimation. This procedure fits a finite autoregression to the time series data, and calculates the spectrum from the estimated autoregression coefficients and the one step prediction error variance. For multivariate time series, the estimated autoregressive matrices and one step prediction covariance matrix produce estimates of the spectra, coherences, phases, and group delays. The use of Akaike's information criterion (AIC) for identification of the order of the autoregression to be used makes the procedure objective. Experience gained from analyzing large amounts of data from the biological and physical sciences has indicated that AIC works very well for model identification when compared to more subjective procedures such as the examination of partialF-statistics. This experience has also indicated that using both autoregressive spectrum estimation and classical spectrum estimation and superimposing the plots gives a much stronger feeling for the shape of the true spectrum being estimated. The results of some of these analyses are presented. View full abstract»

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  • Identification of multiinput-multioutput transfer function and noise model of a blast furnace from closed-loop data

    Page(s): 944 - 951
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    A procedure is described for the identification of multiinput-multioutput transfer function and the disturbance model from closed-loop data. The basic step of the identification procedure is to obtain a multivariate time series model for the input and the output series. A new method-viz., the method of successive orthogonalization is given for the modeling of multiple time series. Real data on closed-loop operation of a blast furnace are analyzed. View full abstract»

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  • Parametrizations of linear dynamical systems: Canonical forms and identifiability

    Page(s): 640 - 646
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    We consider the problem of what parametrizations of linear dynamical systems are appropriate for identification (i.e., so that the identification problem has a unique solution, and all systems of a particular class can be represented). Canonical forms for controllable linear systems under similarity transformation are considered and it is shown that their use in identification may cause numerical difficulties, and an alternate approach is proposed which avoids these difficulties. Then it is assumed that the system matrices are parametrized by some unknown parameters from a priori system knowledge. The identiability of such an arbitrary parametrization is then considered in several situations. Assuming that the system transfer function can be identified asymptotically, conditions are derived for local and global identifiability. Finally, conditions for identifiability from the output spectral density are given for a system driven by unobserved white noise. View full abstract»

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  • Parameter estimation for power systems in the steady state

    Page(s): 882 - 886
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    This paper addresses itself to the variety of model inaccuracy problems arising in the implementation of on-line state estimation for power system bulk transmission networks. Two classes of models are investigated for which parameter estimation algorithms are derived and simulated. The first class consist of network and measurement system parameters of the system being monitored. The second class corresponds to the network equivalent parameters of all external systems. It is shown that telemetry data obtained by the internal system can provide the basis for both types of estimation. A significant factor here is the use made of scheduled and/or forced system outage information in the estimation of the network equivalent. View full abstract»

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  • Code for a general purpose system identifier and evaluator (GPSIE)

    Page(s): 852 - 854
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    The modeling process may be viewed as a three-step iteration: 1) hypothesize a structure, 2) estimate (identify) unknown parameters, and 3) test for consistency between the model and available data. This paper describes a new, publicly-available computer program which performs the second and third tasks. The program, called General Purpose System Identifier and Evaluator (GPSIE), can handle nonlinear, time-varying, multiple input-output systems of arbitrary dimensions. The user supplies an array of data and a subprogram in PL/I or Fortran defining the model structure of interest. GPSIE searches for the maximum-likelihood estimates of any unknown parameters, and computes statistical measures of consistency between the model and the data. Options allow the user to deal efficiently with many kinds of systems. View full abstract»

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  • On the spectral factorization of nonstationary vector random processes

    Page(s): 674 - 679
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    Conditions which depend on the covariance of a vector random process, sufficient to ensure the process can be generated by a linear, invertible system of finite order driven by white noise are derived, and equations which determine the parameters of the system are found. Some structural properties of lumped covariances are given; these stress the close relation between the structure of linear systems and that of lumped covariances and provide a means of establishing the minimal order of generating systems. View full abstract»

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  • Parameter estimation in the canine cardiovascular system

    Page(s): 927 - 931
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    A new method of parameter estimation has been developed to estimate a newly chosen group of eight parameters of the canine arterial system. These parameters, which are physically meaningful, include arterial radii, Young's modulus for the aortic wall, aortic length, and peripheral resistances. The method is model-based, and depends upon minimization of a criterion which combines weighted integrals of absolute values of pressure and flow with errors in certain average and peak values of waveforms. A minimization algorithm which is a modification of the Hooke and Jeeves pattern search method was used. The entire scheme was implemented on a hybrid computer. This method of parameter estimation was developed and checked by use of model-to-model studies. Then dog-to-model parameter estimation runs were made, using data recorded in a dog experiment. Estimated parameters were found to check direct measurements where these were possible, and variations in estimated parameters from data obtained after various drug interventions were found to fall into expected patterns. Future applications of the methods developed would appear to have promise for obtaining rapid estimates of human parameters from noninvasive measurements made in clinical situations. View full abstract»

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  • Identification of linear, multivariable systems operating under linear feedback control

    Page(s): 836 - 840
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    The possibility of estimating process parameters using input-output data collected when the system operates in closed loop is discussed in this paper. Concepts that are useful for a systematic treatment of the problem are introduced. The results refer to the case where the regulator is a linear feedback law or alternates between several such laws. It is shown that a straightforwardly applied identification scheme has the same identifiability properties as the more complex method in which the parameters of the closed-loop system are estimated first. It is also shown that it is always possible to achieve the same identifiability properties as for open-loop systems by shifting between different linear regulators. The required number of regulators depends only on the number of inputs and outputs. The results obtained are illustrated by a numerical example. View full abstract»

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  • On the structure of a class of nonlinear systems

    Page(s): 701 - 706
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    Nonlinear systems composed of linear dynamic sub-systems in cascade with static power nonlinearities are considered. A structure theory is developed based on the representation of input/output behavior by rational functions in several variables. An algorithm is given which solves the minimal complete realization problem for two forms of the representation. Examples are worked in detail to illustrate the results. View full abstract»

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  • Biological modeling with variable compartmental structure

    Page(s): 922 - 926
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    Many biological processes such as water balance and temperature regulation may be modeled by a variable-structure compartmental system of ordinary differential equations. In some cases the system is naturally bilinear, and in others the bilinear system may be a valid approximation of a complex nonlinear process. Tracer dynamics and associated methodology, which can be used to realize certain properties of the system, are analyzed. The theory is applied to tracer experiments for the development of a mammalian water-balance model. View full abstract»

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  • Identification and estimation in econometric systems: A survey

    Page(s): 855 - 862
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    This is an introductory survey of some of the ideas and methods in the identification and estimation of simultaneous equation systems in econometrics. After pointing out the special features of econometric systems, it defines the problem of identification and presents several methods for estimating the parameters in such systems. Hopefully such a survey will be useful to research workers in related fields including control engineering and statistics who are interested in the estimation of dynamic systems and wish to find out whether the works of econometricians are relevant to their own research. This is not a substitute for a treatise in econometrics, but it may help a researcher in a related field decide whether the techniques developed for dynamic econometric systems are useful for his purpose, whether he should study them in depth, and whether he can contribute to improving them. Some research topics will be suggested later in our discussion. View full abstract»

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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

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