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  • 1.
    Forsman, Krister
    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.
    Millnert, Mille
    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.
    Merging 'Reasoning' and Filtering in a Bayesian Framework - Some Sensitivity and Optimality Aspects1991In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 5, no 2, p. 93-106Article in journal (Refereed)
    Abstract [en]

    An approach is described how to incorporate knowledge of symbolic/logic character into a conventional framework of noisy observations in dynamical systems. The idea is based on approximating the optimal solution that could theoretically be computed if a complete Bayesian framework were known (and infinite computational power were available). The nature of the approximations, the deviations from optimality and the sensitivity to ad hoc parameters are specifically addressed. This merging of logic and numerics is essential in many problems of adaptation in control and signal processing.

  • 2.
    Gunnarsson, Svante
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Some Asymptotic Results in Recursive Identification using Laguerre Models1991In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 5, no 5, p. 313-333Article in journal (Refereed)
    Abstract [en]

    This paper deals with recursive identification of time-varying systems using Laguerre models. Laguerre models generalize finite impulse response (FIR) models by using a priori information about the dominating time constants of the system to be identified. Three recursive algorithms are considered: the stochastic gradient algorithm, the recursive least squares algorithm and a Kalman-filter-like recursive identification algorithm. Simple and explicit expressions for the model quality are derived under the assumptions that the system varies slowly, that the model is updated slowly and that the model order is high. The derived expressions show how the use of Laguerre models affects the model quality with respect to tracking capability and disturbance rejection.

  • 3.
    Guo, Lei
    et al.
    Academica Sinica, China.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Priouret, Pierre
    Université Pierre et Marie Curie, France.
    Performance Analysis of the Forgetting Factor RLS Algorithms1993In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 7, no 6, p. 525-237Article in journal (Refereed)
    Abstract [en]

    An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ2/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.

  • 4.
    Krishnamurthy, Vikram
    et al.
    Australian National University, Australia.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Moore, John B.
    Australian National University, Australia.
    Factorizations that Relax the Positive Real Condition in Continuous-time and Fast-Sampled ELS Schemes1990In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 4, no 5, p. 389-414Article in journal (Refereed)
    Abstract [en]

    This paper proposes extended least-squares (ELS) for ARMAX model identification of continuous-time and certain discrete-time systems. The schemes have a relaxed strictly positive real (SPR) condition for global convergence. The relaxed SPR scheme is achieved by introducing overparametrization and prefiltering but without introducing ill-conditioning. The schemes presented are the first such proposed for continuous-time systems.The concepts developed in continuous time carry through to fast-sampled continuous-time systems and associated discrete-time ELS algorithms. For such situations, in comparison with previously proposed discrete-time schemes, the degree of overparametrization required in the proposed scheme of this paper is significantly lower. The reduction is achieved by using more suitable prefiltering and overparametrization techniques than previously proposed.We also establish the persistence of excitation (PE) of the regression vectors in the proposed ELS schemes to assure strong consistency, obtain convergence rates and provide robustness to unmodelled dynamics. To prove the PE of continuous-time regression vectors, we develop output reachability characterization for MIMO linear continuous-time systems.

  • 5.
    Lindskog, Peter
    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.
    Tools for Semiphysical Modelling1995In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 9, no 6, p. 509-523Article in journal (Refereed)
    Abstract [en]

    Semiphysical modelling is often interpreted as an application of system identification where physical insight into the application is used to come up with suitable non-linear transformations of the raw measurements so as to allow for a good model structure. This modelling procedure is less ‘ambitious’ than those used for traditional physical modelling in that no complete physical structure is sought, just suitable inputs and outputs that can be subjected to more or less standard model structures such as linear regressions. In this paper we discuss a semiphysical modelling procedure and various tools supporting it. These include constructive algorithms originating from commutative and differential algebra as well as more informal tools such as the programming environment.

  • 6.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Discussion of Model Accuracy in System Identification1992In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 6, no 3, p. 161-171Article in journal (Refereed)
    Abstract [en]

    Model quality and model accuracy are of prime interest in system identification. In this contribution we will review and discuss these concepts. In particular we will split model errors into contributions from a ‘random error’ and a ‘bias error’ and describe and discuss how to assess these two concepts.

  • 7.
    Ljung, Lennart
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    A personal recollection of Tsypkin2001In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 15, no 2, p. 120-120Other (Other academic)
  • 8.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Recursive Least Squares and Accelerated Convergence in Stochastic Approximation Schemes2001In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 15, no 2, p. 169-178Article in journal (Refereed)
    Abstract [en]

    The so-called accelerated convergence is an ingenuous idea to improve the asymptotic accuracy in stochastic approximation (gradient based) algorithms. The estimates obtained from the basic algorithm are subjected to a second round of averaging, which leads to optimal accuracy for estimates of time-invariant parameters. In this contribution, some simple calculations are used to get some intuitive insight into these mechanisms. Of particular interest is to investigate the properties of accelerated convergence schemes in tracking situations. It is shown that a second round of averaging leads to the recursive least-squares algorithm with a forgetting factor. This also means that in case the true parameters are changing as a random walk, accelerated convergence does not, typically, give optimal tracking properties.

  • 9.
    Ljung, Lennart
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Priouret, Pierre
    Université Paris VI, France.
    A Result of the Mean Square Error Obtained using General Tracking Algorithms1991In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 5, no 4, p. 231-348Article in journal (Other academic)
    Abstract [en]

    Tracking time-varying properties is of crucial importance in all adaptive algorithms. In this contribution we study a fairly general algorithm for tracking properties of model parameters that can be described in a linear regression form (including AR models and the like). An explicit expression for the mean square error between the estimated and the true (time-varying) parameter is established. For slow adaptation this expression can be arbitrarily well approximated by a much simpler expression. The treatment differs from other related studies using weak convergence theory, averaging, etc. in that the results are not asymptotic in nature and are applicable also to the transient phase as well as over unbounded time intervals.

  • 10.
    Ljung, Lennart
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Priouret, Pierre
    Pierre-and-Marie-Curie University, France.
    Remarks on the Mean Square Tracking Error1991In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 5, no 6, p. 395-403Article in journal (Refereed)
    Abstract [en]

    The purpose of this paper is to extend recent results of Ljung and Priouret to a more general class of regressors.

  • 11.
    Stenman, Anders
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Forsman, Krister
    ABB Automation Systems AB, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Segmentation-Based Method for Detection of Stiction in Control Valves2003In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 17, no 7-9, p. 625-634Article in journal (Refereed)
    Abstract [en]

    A method for detecting static friction (stiction) in control valves is proposed. The method is model-based and is inspired by ideas from the fields of change detection and multi-model mode estimation. Opposed to existing methods only limited process knowledge is needed and it is not required that the loop has oscillating behaviour. The advantage of the method is illustrated in both numerical simulations and evaluations on real loop data.

  • 12.
    Åslund, Jan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Biteus, Jonas
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Krysander, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nielsen, Lars
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Safety analysis of autonomous systems by extended fault tree analysis2007In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 21, no 2-3, p. 287-298Article in journal (Refereed)
    Abstract [en]

    Safety is of major concern in many autonomous functions in automotive systems and aerospace. In these application areas, it is standard to use fault trees, and a natural question in many modern systems that include sub-systems like diagnosis, fault-tolerant control, and autonomous functions is how to include the performance of these algorithms in a fault tree analysis for safety. Many possibilities exist but here a systematic way is proposed. It is shown both how safety can be analysed and how the interplay between algorithm design in terms of missed detection rate and false alarm rate is included in the fault tree analysis. Examples illustrate analysis of diagnosis system requirement specification and algorithm tuning. Copyright © 2006 John Wiley & Sons, Ltd.

1 - 12 of 12
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