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Maximum likelihood gradient-based iterative estimation for closed-loop Hammerstein nonlinear systems
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DOI：10.1002/rnc.7065.png)
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Abstract
En 中文
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.
Keywords:
closed-loop Hammerstein system
data window
iterative identification theory
maximum likelihood principle
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