A global convergent outlier robust adaptive predictor for MIMO Hammerstein models

Filipović, Vojislav

doi:10.1002/rnc.3705

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Such idea is applied for design of robust predictor for Hammerstein model. 18 To robust identification, by using stochastic approximation, is devoted Reference 19.…”

Section: Introductionmentioning

confidence: 99%

Outlier robust identification of dual‐rate Hammerstein models in the presence of unmodeled dynamics

Filipović

2021

Intl J Robust & Nonlinear

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The article considers the outlier-robust recursive least squares algorithm for identification of SISO (single-input single-output) dual-rate Hammerstein model. The output sampling period is an integer multiple of the input sampling period. By using polynomial transformation technique we get a model which is suitable for parameter estimation for dual-rate measurement data. After that deterministic dual-rate Hammerstein model is extended with stochastic disturbance (which includes outliers) and unmodeled dynamics. Synthesis of identification algorithm is based on Newton-Raphson method which requires that loss function should be twice differentiable. Huber loss function, relevant for outliers treatment, has just first derivative. In order to overcome problem pseudo-Huber loss function is introduced. This function behaves similarly to Huber loss function and has derivatives of arbitrary orders. Given recursive algorithm is based on synergy of Huber and pseudo-Huber loss functions. Then we consider the robustness of algorithm. The main contributions of the article are: (i) determination of dual-rate Hammerstein model which includes stochastic disturbance with outliers and unmodeled dynamics; (ii) derivation of robust recursive algorithm; (iii) determination of robustness for parameter estimate.

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“…Such idea is applied for design of robust predictor for Hammerstein model. 18 To robust identification, by using stochastic approximation, is devoted Reference 19.…”

Section: Introductionmentioning

confidence: 99%

Outlier robust identification of dual‐rate Hammerstein models in the presence of unmodeled dynamics

Filipović

2021

Intl J Robust & Nonlinear

Self Cite

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“…The actual research in the field of identification devotes considerable attention to robust identification methods [4]. The robust identification of multi-input multi-output models using the stochastic approximation is considered in [5] and the robust adaptive prediction is presented in [6]. The key ingredient in the robust statistics is the Huber loss function.…”

Section: Introductionmentioning

confidence: 99%

Robust Recursive Identification of Hammerstein Models Based on Weiszfald Algorithm

Filipović

2020

FU Aut Cont Rob

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The Hammerstein models can accurately describe a wide variety of nonlinear systems (chemical process, power electronics, electrical drives, sticky control valves). Algorithms of identification depend, among other, on the assumption about the nature of stochastic disturbance. Practical research shows that disturbances, owing the presence of outliers, have a non-Gaussian distribution. In such case it is a common practice to use the robust statistics. In the paper, by analysis of the least favourable probability density, it is shown that the robust (Huber`s) estimation criterion can be presented as a sum of non-overlapping - norm and - norm criteria. By using a Weiszfald algorithm - norm criterion is converted to - norm criterion. So, the weighted - norm criterion is obtained for the identification. The main contributions of the paper are: (i) Presentation of the Huber`s criterion as a sum of - norm and - norm criteria; (ii) Using the Weiszfald algorithm – norm criterion is converted to a weighted - norm criterion; (iii) Weighted extended least squares in which robustness is included through weighting coefficients are derived for NARMAX (nonlinear autoregressive moving average with exogenous variable) . The illustration of the behaviour of the proposed algorithm is presented through simulations.

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Parsimonious Model Based Consistent Subspace Identification of Hammerstein Systems Under Periodic Disturbances

Hou

2024

Int. J. Control Autom. Syst.

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A global convergent outlier robust adaptive predictor for MIMO Hammerstein models

Cited by 4 publications

References 25 publications

Outlier robust identification of dual‐rate Hammerstein models in the presence of unmodeled dynamics

Outlier robust identification of dual‐rate Hammerstein models in the presence of unmodeled dynamics

Robust Recursive Identification of Hammerstein Models Based on Weiszfald Algorithm

Parsimonious Model Based Consistent Subspace Identification of Hammerstein Systems Under Periodic Disturbances

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