Recursive maximum likelihood estimation with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1769" altimg="si5.svg"><mml:mi>t</mml:mi></mml:math>-distribution noise model

Sun, Lu; Ho, Weng Khuen; Ling, Keck Voon; Chen, Tengpeng; Maciejowski, Jan M.

doi:10.1016/j.automatica.2021.109789

Cited by 6 publications

(1 citation statement)

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“…24 This article applies the hierarchical identification principle to study the gradient iterative algorithm for closed-loop Hammerstein nonlinear systems based on the maximum likelihood principle. [25][26][27] The key is to separate the linear block parameters from the nonlinear block parameters by utilizing the key term separation technique, 28 to divide the bilinear parameter model into two subsystems by using the hierarchical identification principle, 29 and to identify the parameter vectors in the two subsystems interactively by combining the gradient search and the iterative estimation theory. 30,31 The main contributions are summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood gradient‐based iterative estimation for closed‐loop Hammerstein nonlinear systems

Xia

2023

Intl J Robust & Nonlinear

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This article studies a new iterative method for a class of closed‐loop Hammerstein systems. The new iterative method solves the crossproducts between the parameters of the linear block and the nonlinear block by using the key term separation technique, decomposes a system into two subidentification models by utilizing the hierarchical identification principle for reduced computational complexity, and maximizes the maximum likelihood cost function by using the input and output data with a data window for improved parameter estimation accuracy. A numerical simulation example and a continuous stirred tank reactor experiment are presented to demonstrate that the proposed algorithm can work effectively.

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