Parametric Identification of Parallel Hammerstein Systems

Schoukens, Maarten; Pintelon, R.; Rolain, Yves

doi:10.1109/tim.2011.2138370

Cited by 55 publications

(41 citation statements)

References 18 publications

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“…2, which is a p=2, n=3 model. Another MDL order selection method in [8] is applied on the system for a comparative study. The choice of the MDL selection method is a p=3, n=5 model, also can be seen in Fig .2.…”

Section: Parametric Identification Of Brushless Motorsmentioning

confidence: 99%

“…However, most previous works rather treat it as a simpler linear dynamical system [2], [3]. Some nonparametric and semi parametric identification methods for Hammerstein series models are already presented in [5], [6]. As our goal is to control the rotor speed utilizing the identified model, a parametric identification method is needed to obtain a fully parametric model.…”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem, a parametric identification method of Hammerstein series models using minimum description length (MDL) criterion is presented in [8]. However, this method can only determine the memory length of model, and it is helpless for selecting the model order.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Modified Nonnegative Garrote Method for Identification of Brushless DC Motors

Jin¹,

Lu²,

Zhu³

2014

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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Abstract-An effective identification framework has presented to build a more accurate nonlinear model for brushless DC motors, which are frequently used as drive systems of micro electromechanical unmanned aerial vehicles. The identification method uses a two-step procedure to obtain a fully parametric model. First, the initial model of system is estimated. Next, a coefficient shrinkage method is used for model structure selection. There are two main highlight processes in this paper. One is the use of Hammerstein series models for identification, which leads to a trade-off between model accuracy and complexity. Another is the use of modified nonnegative garrote method for model reduction and finding the true model order. The proposed method is validated on simulated system and finally used to identify a brushless DC motor system. The obtained nonlinear model of brushless DC motor is of high performance and controllable model complexity.

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Section: Parametric Identification Of Brushless Motorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Nonnegative Garrote Method for Identification of Brushless DC Motors

Jin¹,

Lu²,

Zhu³

2014

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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“…A complete discussion of the identification techniques used to obtain estimates for G + n (ω) and α [n] can be found in Schoukens et al (2010). In short, the system is excited with a series of multisine input signal which differ in excitation level.…”

Section: Example 2: Broadband Identification Of Hosidfsmentioning

confidence: 99%

Spectral Analysis of Block Structured Nonlinear Systems

Rijlaarsdam

Nuij

Schoukens

et al. 2011

IFAC Proceedings Volumes

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“…This approach often suffers from the ill-conditioned problem brought from the least square estimator. In [8] a method can quickly access the structural elements of Hammerstein series is presented. By using exponential sine sweeps as input signals, this method separates the different orders of nonlinearity in temporal domain.…”

Section: Introductionmentioning

confidence: 99%

A Model of Second Order Hammerstein Series for Nonlinear System

Jin¹,

Lu²

2016

Advances in Computer Science Research

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Abstract. In this paper, modeling a weakly nonlinear system whose nonlinearity is up to the second order is studied. During this task a second order Hammerstein series model is used because it can lead to a trade-off between computational cost and generalization ability. To extract such model form measured input-output data, a nonparametric algorithm based on the minimum mean square error criterion is proposed. The polyspectrum up to order 4 is used to determine the Hammerstein kernels, while the input is a random multi-sine signal. Finally the proposed modeling method is validated on simulated system.

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Parametric Identification of Parallel Hammerstein Systems

Cited by 55 publications

References 18 publications

A Modified Nonnegative Garrote Method for Identification of Brushless DC Motors

A Modified Nonnegative Garrote Method for Identification of Brushless DC Motors

Spectral Analysis of Block Structured Nonlinear Systems

A Model of Second Order Hammerstein Series for Nonlinear System

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