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  • 1.
    Albertos, Pedro
    et al.
    Polytechnical University of Valencia, Spain.
    Goodwin, Graham C.
    University of Newcastle, Australia.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Pseudo Linear Regression Algorithm for On-Line Parameter Estimation with Missing Data1992Report (Other academic)
  • 2.
    Andersson, Torbjörn
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Pucar, Predrag
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Projekt operatörsverktyg, delprojekt 4: Modeller för massatransport och beräkning av uppehållstid i fiberlinjen. Slutrapport1992Report (Other academic)
  • 3.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf J.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Best Choice of Coordinate System for Tracking Coordinated Turns1995Report (Other academic)
    Abstract [en]

    A standard approach to tracking is to use the extended Kalman filter (EKF) applied to a nonlinear state-space model. We compare two conceivable choices of state variables for modeling civil aircrafts. One where Cartesian velocities are used and one where absolute velocity and heading angle are used. In both choices, Cartesian coordinates are used for position and angular velocity for turning. It is shown that the latter state vector always performs better. This is proven by considering the linearization error made in the extended Kalman filter applied either to a time-continuous model or a discretized model. The result is supported by a Monte Carlo simulation study.

  • 4.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Recursive EM Algorithm for Identification of ARX-Models Subject to Missing Data1992Report (Other academic)
  • 5.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    An Extension of the Simulation and Analysis Program ANLAB1983Report (Other academic)
  • 6.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    An On-Line Threshold Selector for Failure Detection1992Report (Other academic)
  • 7.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Analysis of Identified 2-D Noncausal Models1991Report (Other academic)
    Abstract [en]

    There are two approaches to the identification of noncausal autoregressive systems in two dimensions differing in the assumed noise model. For both approaches, the maximum likelihood estimator formulated in the frequency domain is presented. The Fisher information matrix is evaluated and found to be the sum of a block-Toeplitz and a block-Hankel matrix. The variance of the parameters, however, cannot be used for comparison of the two approaches, so the variance in the frequency domain is evaluated, assuming that the true system in each case can be described by a model of that type, possibly high-order. In particular, the variance of the spectrum estimate is derived. If the number of parameters tends to infinity, it is shown that the two approaches give the same spectrum estimate variance. The question of which set of true spectra can be described by the respective approaches is discussed.

  • 8.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Analysis of Identified 2-D Noncausal Models1991Report (Other academic)
    Abstract [en]

    There are two approaches to the identification of noncausal autoregressive systems in two dimensions differing in the assumed noise model. For both approaches, the maximum likelihood estimator formulated in the frequency domain is presented. The Fisher information matrix is evaluated and found to be the sum of a block-Toeplitz and a block-Hankel matrix. The variance of the parameters, however, cannot be used for comparison of the two approaches, so the variance in the frequency domain is evaluated, assuming that the true system in each case can be described by a model of that type, possibly high-order. In particular, the variance of the spectrum estimate is derived. If the number of parameters tends to infinity, it is shown that the two approaches give the same spectrum estimate variance. The question of which set of true spectra can be described by the respective approaches is discussed.

  • 9.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    ANLAB: The Manual1985Report (Other academic)
  • 10.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Digital Protective Relaying through Recursive Least Squares Identification1987Report (Other academic)
    Abstract [en]

    Parameter estimation is applied to the problem of transmission line protection. The Fourier coefficients of voltage and current are estimated using recursive least-squares identification. The estimates are then used to detect short circuits. The method is evaluated using data generated by the program EMTP

  • 11.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Estimate of Average Residence Time given an Identified ARX-Model1992Report (Other academic)
  • 12.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Estimation of Average Residence Time Given an Identified AR-Model1992Report (Other academic)
  • 13.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Frequency Domain Accuracy of Identified 2-D Causal Models1989In: Proceedings of the 1989 International Conference on Acoustics, Speech and Signal Processing, 1989, p. 2294-2297Conference paper (Refereed)
    Abstract [en]

    A study is made of parametric estimation of 2-D transfer functions using the least-squares method which is the dominant method for causal systems. The analysis concentrates on the frequency-domain accuracy of the estimated models. There exist results for the accuracy of the parameter estimates. The parameters are asymptotically Gaussian distributed. The variance of the polynomial in the frequency domain can be expressed using these results for the parameters. This, however, gives no insight into the dependence on the true transfer function. An illuminating result is obtained if the model order tends to infinity. The limiting results show good correspondence with Monte-Carlo simulations for small data sets obtained with low model orders.

  • 14.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Frequency Domain Accuracy of Identified 2-D Causal Models1990Report (Other academic)
    Abstract [en]

    A study is made of parametric estimation of 2-D transfer functions using the least-squares method which is the dominant method for causal systems. The analysis concentrates on the frequency-domain accuracy of the estimated models. There exist results for the accuracy of the parameter estimates. The parameters are asymptotically Gaussian distributed. The variance of the polynomial in the frequency domain can be expressed using these results for the parameters. This, however, gives no insight into the dependence on the true transfer function. An illuminating result is obtained if the model order tends to infinity. The limiting results show good correspondence with Monte-Carlo simulations for small data sets obtained with low model orders.

  • 15.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Identification of Time Varying Systems and Application of System Identification to Signal Processing1986Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Part I

    A new approach to identification of time varying systems is presented, and evaluated using computer simulations. The new approach is built upon the similarities between recursive least squares identification and Kalman filtering.

    The parameter variations are modelled as process noise in a state space model and then identified using adaptive Kalman filtering. A method for adaptive Kalman filtering is derived and analysed. The simulations indicate that this new approach is superior to previous methods based on adjusting the forgetting factor. This improvement is however gained at the price of a signification increase in computational complexity.

    Part II

    In this part we apply parameter estimation to the problem of transmission line protection.

    One approach based on recursive least squares identification is presented. The method has ben tested using simulated data generated by the program EMTP.

    Another approach based on the theory of travelling waves is also discussed.

    Part III

    In this part a method for input estimation or deconvolution is presented. The basis of the method is to use a parametrized model the input signal. To use the method we should thus be able to express the input signal as a function of some unknown parameters and time. The algorithms simultaneously estimates the parameters of the input signal and the parameters of the system transfer function. The presentation here is restricted to transfer functions of all pole type, i.e. ARX-models. The method can be extended to handle zeros in the transfer function. The computational burden would however increase significantly. The algorithm uses efficient numerical methods, as for instance QR-factorization thorugh Householder transformation.

    The algorithm is in this paper applied to a problem in speech coding. It has been observed that the quality of synthesized speech can be improved, if a more detailed model than an impulse train is used for the pitch pulses, see Fant (1980). It is here shown how the method presented in this paper can be used to estimate the system parameters of the speech production and the parameters of the glottal pulse simultaneously.

  • 16.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Identification of Time Varying Systems Through Adaptive Kalman Filtering1987In: Proceedings of the 10th IFAC World Congress, 1987Conference paper (Refereed)
  • 17.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Identification of Time Varying Systems through Adaptive Kalman Filtering1987Report (Other academic)
  • 18.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Missing Data M-Files: User's Guide1992Report (Other academic)
  • 19.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On System Identification in one and two Dimensions with Signal Processing Applications1988Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis consists of four parts, with system identification as the common theme. The first part studies the asymptotic properties of two-dimensional identification methods. In the second part an approach to identification of time varying systems is presented. Part three applies system identification to the problem of transmission line protection. Finally part four deals with input estimation in speech coding.

    Part I is devoted to system identification in two dimensions. First we study the asymptotic properties of the estimates as the number of data tends to infinity. The main objective is to investigate what happens if the model order also tends to infinity. The focus is on frequency expressions of the extimation variance. The analysis covers both the least squares method for causal models, and the maximum likelihood method for noncausal models.

    In Part II we study one approach to identification of time varying sytems. The parameter variations are modelled as process noise in a state space model, and identified using adaptive Kalman filtering. A method for adaptive Kalman filtering is derived and analysed. The simulations indicate that this new approach is superior to previous methods based on adjusting the forgetting factor. The improvement is however gained at the price of a significant increase in computational complexity.

    Part III describes the use of recursive identification in protective relaying. The Fourier coefficients of voltage and current are estimated using recursive least squares identification. The estimates are then used to detect short circuits. The method is evaluated using data generated by the standard program EMTP.

    In Part IV a method for inverse glottal filtering is presented. The basis of the method is to use a parameterized model of the input signal, i.e. the glottal pulses. The algorithm simultaneously estimates the parameters of the input signal and the parameters of the system transfer function, the vocal tract model. The presentation is restricted to transfer functions of all-pole type.

  • 20.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On the Use of Linear Interpolation in Identification Subject to Missing Data1992Report (Other academic)
  • 21.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    System Identification Subject to Missing Data1990Report (Other academic)
    Abstract [en]

    In this paper we study parameter estimation when the measurement information may be incomplete. As a basic system representation we use an ARX-model. The presentation covers both missing output and input. First reconstruction of the missing values is discussed. The reconstruction is based on a state-space formulation of the system, and is performed using the Kalman filtering or fixed-interval smoothing formulas. Several approaches to the identification problem are then presented, including a new method based on the so called EM algorithm. The different approaches are tested and compared using Monte-Carlo simulations. The choice of method is always a trade off between estimation accuracy and computational complexity. According to the simulations the gain in accuracy using the EM method can be considerable if much data are missing.

  • 22.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    System Identification Subject to Missing Data1991Report (Other academic)
    Abstract [en]

    In this paper we study parameter estimation when the measurement information may be incomplete. As a basic system representation we use an ARX-model. The presentation covers both missing output and input. First reconstruction of the missing values is discussed. The reconstruction is based on a state-space formulation of the system, and is performed using the Kalman filtering or fixed-interval smoothing formulas. Several approaches to the identification problem are then presented, including a new method based on the so called EM algorithm. The different approaches are tested and compared using Monte-Carlo simulations. The choice of method is always a trade off between estimation accuracy and computational complexity. According to the simulations the gain in accuracy using the EM method can be considerable if much data are missing.

  • 23.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Graebe, Stefan F.
    University of Newcastle, Australia.
    Model Reduction for PID Control1992Report (Other academic)
  • 24.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Granberg, Bertil
    Identifications on Data from the Skoghall Pulp Mill (In Swedish)1991Report (Other academic)
  • 25.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Bayesian Approach to Manoeuvre Tracking and Detection1992Report (Other academic)
  • 26.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Multiple Model Approach to Manouvre Tracking and Detection1992Report (Other academic)
  • 27.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hill, David J.
    University of Newcastle, Australia.
    Power Plant Monitoring: Survey and Directions for Future Research1992Report (Other academic)
  • 28.
    Isaksson, Alf J.
    Linköping University, The Institute of Technology. Linköping University, Department of Management and Engineering, Production Economics. ABB AB, Corporate Research, SE-721 78 Västerås, Sweden.
    Some aspects of industrial system identification2013Conference paper (Refereed)
    Abstract [en]

    The most important and time consuming part of an industrial application of control is the modelling. It may take 50 per cent or more of the entire project. Therefore a major challenge for a control systems supplier like ABB is to constantly try to decrease the engineering effort for modelling.

    This paper discusses some different aspects of modelling and identification originating from application in many different industries such as pulp and paper, rolling mills, power plants and specialty chemicals.

  • 29.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Strömberg, Jan-Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Recursive Construction of Trees as Models of Dynamical Systems1991Report (Other academic)
    Abstract [en]

    An issue that is of importance for control applications is discussed: how to construct the trees online, i.e. recursively, as more and more data become available. A theorem regarding recursive tree-building is stated and proved, and implementation issues are considered.

  • 30.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Millnert, Mille
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Input Estimation with Application to Speech Coding1984Report (Other academic)
  • 31.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Millnert, Mille
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Input Estimation with Application to Speech Coding1987Report (Other academic)
    Abstract [fr]

    Dans ce papier une méthode pour l'estimation de l'entrée (ou déconvolution) est présentée. La méthode est basée principalement sur l'utilisation d'une certaine paramétrization du modèle du signal d'entrée. Pour utiliser cette méthode, nous devons être capable d'exprimer le signal d'entrée en fonction de quelques paramètres inconnues et du temps. L'algorithme est conçu pour estimer, simultanément, les paramètres du signal d'entrée et ceux de la fonction de transfert du système. On se limite à l'étude des systèmes dont la fonction de transfert ne comportant que des pôles (c.à.d modèles ARX). La méthode peut être étendue pour consider aussi les zéros de la fonction de transfert. Il est évident que ceci entraîne une augmentation de la charge numérique. L'algorithme est basé sur des méthodes numériques efficaces comme par exemple la factorisation QR utilisant les transformations de Householder. L'application d'un tel algorithme au codage de la parole est présentée. It est à noter que la qualité du signal synthétisé de la parole, peut être nettement améliorée si un modèle plus détaillé est utilisé pour décrire, le modèle du mouvement des cordes vocal plutôt qu'un train d'impulsion. On montre aussi que la méthode envisagée peut être utilisée pour estimer les paramètres du système vocales et ceux du modèle du mouvement des cordes vocales simultanément.

  • 32.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Millnert, Mille
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Inverse Glottal Filtering using a Parameterized Input Model1989In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 18, no 4, p. 435-445Article in journal (Refereed)
    Abstract [en]

    In this paper computational algorithms for inverse glottal filtering are studied. The objective of inverse glottal filtering is to estimate the driving source. A good model for the glottal pulse is useful for, e.g., speech synthesis, speech recognition and speaker diagnostics. One common approach is to use a parameterized model of the input signal, i.e., the glottal pulses. The algorithm presented enables simultaneous estimation of the parameters of the input signal and the parameters of the system transfer function, the vocal tract model. The presentation here is restricted to transfer functions of all-pole type, i.e., AR-models. The method can be extended to handle zeros in the transfer function. The computational burden would, however, increase significantly. The algorithm uses efficient numerical methods, as, for instance, QR-factorization through Householder transformations.

  • 33.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Millnert, Mille
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Inverse Glottal Filtering using a Parameterized Input Model1987Report (Other academic)
    Abstract [en]

    In this paper computational algorithms for inverse glottal filtering are studied. The objective of inverse glottal filtering is to estimate the driving source. A good model for the glottal pulse is useful for, e.g., speech synthesis, speech recognition and speaker diagnostics. One common approach is to use a parameterized model of the input signal, i.e., the glottal pulses. The algorithm presented enables simultaneous estimation of the parameters of the input signal and the parameters of the system transfer function, the vocal tract model. The presentation here is restricted to transfer functions of all-pole type, i.e., AR-models. The method can be extended to handle zeros in the transfer function. The computational burden would, however, increase significantly. The algorithm uses efficient numerical methods, as, for instance, QR-factorization through Householder transformations.

  • 34.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Corp Research, Sweden.
    Sjöberg, Johan
    Linköping University, Department of Electrical Engineering. Volvo Construct Equipment, Sweden.
    Tornqvist, David
    SenionLab AB, S-58330 Linkoping, Sweden.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Kok, Manon
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Using horizon estimation and nonlinear optimization for grey-box identification2015In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 30, p. 69-79Article in journal (Refereed)
    Abstract [en]

    An established method for grey-box identification is to use maximum-likelihood estimation for the nonlinear case implemented via extended Kalman filtering. In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that, in the linear case, horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. For the nonlinear case two special cases are presented where the bias correction can be determined without approximation. A procedure how to approximate the bias correction for general nonlinear systems is also outlined. (C) 2015 Elsevier Ltd. All rights reserved.

  • 35.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. ABB AB, Sweden.
    Törnqvist, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sjöberg, Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization2009In: Proceedings of the 41st ISCIE International Symposium on Stochastic Systems, Institute of Systems, Control and Information Engineers , 2009, p. 1-6Conference paper (Refereed)
    Abstract [en]

    In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. A procedure how to approximate the bias correction for nonlinear systems is outlined.

  • 36.
    Isaksson, Alf
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Törnqvist, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sjöberg, Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization2010Report (Other academic)
    Abstract [en]

    In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. A procedure how to approximate the bias correction for nonlinear systems is outlined.

  • 37.
    Ljung, Lennart
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wallin, Ragnar
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    An Iterative Method for Identification of ARX Models from Incomplete Data2000In: Proceedings of the 39th IEEE Conference on Decision and Control, IEEE , 2000, p. 203-208 vol.1Conference paper (Refereed)
    Abstract [en]

    This paper describes a very simple and intuitive algorithm to estimate parameters of ARX models from incomplete data sets. An iterative scheme involving two least squares steps and a bias correction is all that is needed.

  • 38.
    Millnert, Mille
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Input Estimation with Application Speech Coding1985In: Proceedings of the 1985 GRETSI Conference, 1985Conference paper (Refereed)
  • 39.
    Millnert, Mille
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ottersten, Björn
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Skeppstedt, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Viberg, Mats
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Methods for Signal Modelling1988In: Proceedings of the 1988 Workshop on Digital Transmission, 1988Conference paper (Other academic)
  • 40.
    Peretzki, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Carvalho Bittencourt, André
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Forsman, Krister
    Perstorp AB, Sweden.
    Data Mining of Historic Data for Process Identification2011In: Proceedings of the 2011 AIChE Annual Meeting, American Institute of Chemical Engineers, 2011, p. 1027-1033Conference paper (Refereed)
    Abstract [en]

    Performing experiments for system identication is often a time-consuming task which may also interfere with the process operation. With memory prices going down, it is more and more common that years of process data are stored (without compression) in a history database. The rationale for this work is that in such stored data there must already be intervals informative enough for system identication. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identication (rather than performing completely automatic system identication). For each loop, 4 stored variables are required; setpoint, manipulated variable, process output and mode of the controller.

    The proposed method requires a minimum of knowledge of the process and is implemented in a simple and ecient recursive algorithm. The essential features of the method are the search for excitation of the input and output, followed by the estimation of a Laguerre model combined with a chi-square test to check that at least one estimated parameter is statistically signicant. The use of Laguerre models is crucial to handle processes with deadtime without explicit delay estimation. The method was tested on three years of data from more than 200 control loops. It was able to find all intervals in which known identication experiments were performed as well as many other useful intervals in closed/open loop operation.

  • 41.
    Peretzki, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Carvalho Bittencourt, André
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Forsman, Krister
    Perstorp AB, Sweden.
    Data Mining of Historic Data for Process Identification2011Report (Other academic)
    Abstract [en]

    Performing experiments for system identication is often a time-consuming task which may also interfere with the process operation. With memory prices going down, it is more and more common that years of process data are stored (without compression) in a history database. The rationale for this work is that in such stored data there must already be intervals informative enough for system identication. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identication (rather than performing completely automatic system identication). For each loop, 4 stored variables are required; setpoint, manipulated variable, process output and mode of the controller.

    The proposed method requires a minimum of knowledge of the process and is implemented in a simple and ecient recursive algorithm. The essential features of the method are the search for excitation of the input and output, followed by the estimation of a Laguerre model combined with a chi-square test to check that at least one estimated parameter is statistically signicant. The use of Laguerre models is crucial to handle processes with deadtime without explicit delay estimation. The method was tested on three years of data from more than 200 control loops. It was able to find all intervals in which known identication experiments were performed as well as many other useful intervals in closed/open loop operation.

  • 42.
    Rosander, Peter
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    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.
    Forsman, Krister
    Perstorp AB, Sweden.
    Performance Analysis of Robust Averaging Level Control2011Report (Other academic)
    Abstract [en]

    Frequent inlet flow changes, especially in the same direction, typically cause problems for averaging level controllers. To obtain optimal flow filtering while being robust towards future inlet flow upsets closed loop robust MPC was used. Its performance and robustness is analyzed and compared to the optimal averaging level controller. The knowledge gained from the robust MPC exercise is also used to propose a robustification of the optimal controller. Both the analysis and the simulation results show that the robust controller obtains comparable flow filtering as the optimal controller even when inlet flow changes are sparse while handling frequent upsets considerably better.

  • 43.
    Rosander, Peter
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    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.
    Forsman, Krister
    Perstorp AB, Sweden.
    Performance Analysis of Robust Averaging Level Control2012In: Proceedings of the 2012 Conference on Chemical Process Control, 2012Conference paper (Refereed)
    Abstract [en]

    Frequent inlet flow changes, especially in the same direction, typically cause problems for averaging level controllers. To obtain optimal flow filtering while being robust towards future inlet flow upsets closed loop robust MPC was used. Its performance and robustness is analyzed and compared to the optimal averaging level controller. The knowledge gained from the robust MPC exercise is also used to propose a robustification of the optimal controller. Both the analysis and the simulation results show that the robust controller obtains comparable flow filtering as the optimal controller even when inlet flow changes are sparse while handling frequent upsets considerably better

  • 44.
    Rosander, Peter
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    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.
    Forsman, Krister
    Perstorp AB, Sweden.
    Practical Control of Surge Tanks Suffering from Frequent Inlet Flow Upsets2011Report (Other academic)
    Abstract [en]

    In the presence of frequent inlet flow upsets, tuning of averaging level controllers is typically quite complicated since not only the size of the individual steps but also the time in between the subsequent steps need to considered. One structured way to achieve optimal filtering for such a case is to use Robust Model Predictive Control. The robust MPC controller is, however, quite computationally demanding and not easy to implement. In this paper two linear controllers, which mimic the behavior of the robust MPC, are proposed. Tuning guidelines to avoid violation of the tank level constraints as well as to achieve optimal filtering are presented.

  • 45.
    Rosander, Peter
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    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.
    Forsman, Krister
    Perstorp AB, Sweden.
    Practical Control of Surge Tanks Suffering from Frequent Inlet Flow Upsets2012In: Proceedings of the 2nd IFAC Conference on Advances in PID Control, 2012, p. 258-263Conference paper (Refereed)
    Abstract [en]

    In the presence of frequent inlet flow upsets, tuning of averaging level controllers is typically quite complicated since not only the size of the individual steps but also the time in between the subsequent steps need to considered. One structured way to achieve optimal filtering for such a case is to use Robust Model Predictive Control. The robust MPC controller is, however, quite computationally demanding and not easy to implement. In this paper two linear controllers, which mimic the behavior of the robust MPC, are proposed. Tuning guidelines to avoid violation of the tank level constraints as well as to achieve optimal filtering are presented.

  • 46.
    Rosander, Peter
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    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.
    Forsman, Krister
    Perstorp AB, Sweden.
    Robust Averaging Level Control2011In: Proceedings of the 2011 AIChE Annual Meeting, 2011Conference paper (Refereed)
    Abstract [en]

    Frequent inlet flow changes typically cause problems for averaging level controllers. For a frequently changing inlet flow the upsets do not occur when the system is in steady state and the tank level at its set-point. For this reason the tuning of the level controller gets quite complicated, since not only the size of the upsets but also the time in between them relative to the hold up of the tank have to be considered. One way to obtain optimal flow filtering while directly accounting for future inlet flow upsets is to use closed-loop robust MPC, as proposed here. The behavior of the robust MPC controller differs from earlier proposed level controllers as it does not return the tank level to a fixed set-point following an inlet flow upset. Guidelines on the tuning of the controller is presented and its performance is compared to that of a previously proposed MPC approach.

  • 47.
    Rosander, Peter
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    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.
    Forsman, Krister
    Perstorp AB, Sweden.
    Robust Averaging Level Control2011Report (Other academic)
    Abstract [en]

    Frequent inlet flow changes typically cause problems for averaging level controllers. For a frequently changing inlet flow the upsets do not occur when the system is in steady state and the tank level at its set-point. For this reason the tuning of the level controller gets quite complicated, since not only the size of the upsets but also the time in between them relative to the hold up of the tank have to be considered. One way to obtain optimal flow filtering while directly accounting for future inlet flow upsets is to use closed-loop robust MPC, as proposed here. The behavior of the robust MPC controller differs from earlier proposed level controllers as it does not return the tank level to a fixed set-point following an inlet flow upset. Guidelines on the tuning of the controller is presented and its performance is compared to that of a previously proposed MPC approach.

  • 48.
    Strömberg, Jan-Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Towards Online Tree Construction1992Report (Other academic)
  • 49.
    Strömberg, Jan-Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Zrida, Jalel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Neural Trees: Using Neural Nets in a Tree Classifier Structure1991In: Proceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing, IEEE , 1991, Vol. 1, p. 137-140Conference paper (Refereed)
    Abstract [en]

    The concept of tree classifiers is combined with the popular neural net structure. Instead of having one large neural net to capture all the regions in the feature space, the authors suggest the compromise of using small single-output nets at each tree node. This hybrid classifier is referred to as a neural tree. The performance of this classifier is evaluated on real data from a problem in speech recognition. When verified on this particular problem, it turns out that the classifier concept drastically reduces the computational complexity compared with conventional multilevel neural nets. It is also noted that these data make it possible to grow trees online from a continuous data stream.

  • 50.
    Strömberg, Jan-Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Zrida, Jalel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Isaksson, Alf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Neural Trees: Using Neural Nets in a Tree Classifier Structure1991Report (Other academic)
    Abstract [en]

    The concept of tree classifiers is combined with the popular neural net structure. Instead of having one large neural net to capture all the regions in the feature space, the authors suggest the compromise of using small single-output nets at each tree node. This hybrid classifier is referred to as a neural tree. The performance of this classifier is evaluated on real data from a problem in speech recognition. When verified on this particular problem, it turns out that the classifier concept drastically reduces the computational complexity compared with conventional multilevel neural nets. It is also noted that these data make it possible to grow trees online from a continuous data stream.

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