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Iterative learning identification: Dynamic parametrization modeling and comparison
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AI
DOI：10.1002/rnc.6618.png)
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Abstract
En 中文
This article elaborates the iterative learning mechanism for time-varying system identification, and describes the learning algorithms that could achieve the consistent estimation for time-varying parameters under persistent repetitive-excitation conditions. A dynamic parametrization approach, in this article, is presented for modeling and analysis a general class of nonlinear systems. The derivations are conducted to give linear-in-the-parameters models with time-varying coefficients. The resultant models can be in a unified form, with the aid of the variable difference representation, and the iterative learning least squares algorithm and its variant are applicable for the purpose of parameter estimation. Moreover, a learning control scheme is adopted for demonstrating effectiveness of the dynamically-parametrized modes, which are simulated and fully compared with the presented numerical results.
Keywords:
iterative learning control
learning algorithms
linear-in-the-parameters models
nonlinear system modeling
time-varying system identification
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