Construction of composite models from observed data

Skeppstedt, Anders; Ljung, Lennart; Millnert, Mille

doi:10.1080/00207179208934230

Cited by 61 publications

(24 citation statements)

References 3 publications

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“…Since the computational complexity of batch algorithms depends on the number of data points, such algorithms may not be suitable for real time applications. An online algorithm for the identification of SARX/PWARX models is proposed in [65]. It exploits a mixture of recursive identification and pattern recognition techniques in order to identify the current parameter values.…”

Section: Recursive Identification Approachesmentioning

confidence: 99%

Identification of Hybrid Systems A Tutorial

Paoletti

Juloski

Ferrari-Trecate

et al. 2007

European Journal of Control

358

271

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Section: Recursive Identification Approachesmentioning

confidence: 99%

Identification of Hybrid Systems A Tutorial

Paoletti

Juloski

Ferrari-Trecate

et al. 2007

European Journal of Control

358

271

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“…This is the so-called black-box identification approach . Black-box identification of local linear model structures has been studied mainly for inputoutput systems and for state space systems of which all states are measurable (Murray-Smith and Johansen, 1997b;Skeppstedt et al, 1992;Cao et al, 1997;Boukhris et al, 1999). The case where only part of the state is measurable is of course of more general interest.…”

Section: Introductionmentioning

confidence: 99%

Identification of composite local linear state-space models using a projected gradient search

Verdult¹,

Ljung²,

Verhaegen³

2002

International Journal of Control

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An identification method is described to determine a weighted combination of local linear state space models from input and output data. Normalized radial basis function are used for the weights, and the system matrices of the local linear models are fully parameterized. By iteratively solving a nonlinear optimization problem, the centers and widths of the radial basis functions and the system matrices of the local models are determined. To deal with the nonuniqueness of the fully parameterized state space system, a projected gradient search algorithm is described. It is pointed out that when the weights depend only on the input, the dynamical gradient calculations in the identification method are stable. When the weights also depend on the output, certain difficulties might arise. The methods are illustrated using several examples that have been studied in the literature before.

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“…While the literature on non linear identification can now provide advanced tools for the estimation of a wide variety of model classes, in such a case it would be useful to separate conventional input variables from scheduling variables (i.e., variables defining the operating point of the plant), by letting them enter the model in distinct ways ( [4], [5]). …”

Section: Introductionmentioning

confidence: 99%

Identification for gain-scheduling: a balanced subspace approach

Lovera

Mercère

2007

2007 American Control Conference

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Abstract-The problem of deriving MIMO parameterdependent models for gain-scheduling control design from data generated by local identification experiments is considered and a numerically sound approach is proposed, based on subspace identification ideas combined with the use of suitable properties of balanced state space realisations. Simulation examples are used to demonstrate the performance of the proposed approach.

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Construction of composite models from observed data

Cited by 61 publications

References 3 publications

Identification of Hybrid Systems A Tutorial

Identification of Hybrid Systems A Tutorial

Identification of composite local linear state-space models using a projected gradient search

Identification for gain-scheduling: a balanced subspace approach

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