Non-linear dynamic system identification: A multi-model approach

Boukhris, Anass; Mourot, Gilles; Ragot, José

doi:10.1080/002071799220795

Cited by 71 publications

(33 citation statements)

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“…The VAF on this validation data set is 97.9 % for the first output and 99.2 % for the second output; MSE for the outputs equals 0.0003 and 0.0016, respectively. The performance of our local linear models is comparable to the performance of the fuzzy model described by Boukhris et al (1999), but with a reduced model complexity. The performance is better than the neural network of Narendra and Parthasarathy (1992) and the fuzzy model of Nie (1994).…”

Section: Mimo Systemsupporting

confidence: 53%

“…Nie (1994) has used this example to demonstrate the dynamic modeling capabilities of neural fuzzy networks and Boukhris et al (1999) used it with local linear fuzzy models. The input-output description of the system is…”

Section: Siso System Scheduled On the Inputmentioning

confidence: 99%

“…The following MIMO example has been taken from Narendra and Parthasarathy (1992) and has also been studied by Nie (1994) and Boukhris et al (1999). The system has two inputs …”

Section: Mimo Systemmentioning

confidence: 99%

“…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%

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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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Section: Mimo Systemsupporting

confidence: 53%

Section: Siso System Scheduled On the Inputmentioning

confidence: 99%

“…The following MIMO example has been taken from Narendra and Parthasarathy (1992) and has also been studied by Nie (1994) and Boukhris et al (1999). The system has two inputs …”

Section: Mimo Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Other weighting functions are introduced in Ref. 12. In this paper, it is assumed that the number of operating points is determined and the weighting functions w i ð(Þ (i ¼ 1; .…”

Section: Polytopic Model and Objective Of Parameter Estimationmentioning

confidence: 99%

Parameter Estimation of Polytopic Models for a Linear Parameter Varying Aircraft System

Fujimori

Ljung

2006

Trans. Japan Soc. Aero. S Sci.

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This paper presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system that has flight velocity as the varying parameter and stability and control derivatives (SCDs) as the model parameters. In an identification simulation, the polytopic model is more suitable for expressing the behaviors of the LPV aircraft system than the LTI model from the viewpoints of time and frequency responses. The SCDs of the initial polytopic model are adjusted to fit the model output to the outputdata obtained from the LPV aircraft system.

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References

2012

Autonomous Learning Systems

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Non-linear dynamic system identification: A multi-model approach

Cited by 71 publications

References 0 publications

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

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

Parameter Estimation of Polytopic Models for a Linear Parameter Varying Aircraft System

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