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Volume 4 Issue 2
Apr.  2017

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Yang Zhao, Yan Li, Fengyu Zhou, Zhongkai Zhou and YangQuan Chen, "An Iterative Learning Approach to Identify Fractional Order KiBaM Model," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 322-331, Apr. 2017. doi: 10.1109/JAS.2017.7510358
Citation: Yang Zhao, Yan Li, Fengyu Zhou, Zhongkai Zhou and YangQuan Chen, "An Iterative Learning Approach to Identify Fractional Order KiBaM Model," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 322-331, Apr. 2017. doi: 10.1109/JAS.2017.7510358

An Iterative Learning Approach to Identify Fractional Order KiBaM Model

doi: 10.1109/JAS.2017.7510358

This work was supported by the Major Scientific Instrument Development Program of the National Natural Science Foundation of China 61527809

the National Natural Science Foundation of China 61374101

the National Natural Science Foundation of China 61375084

and the Young Scholars Program of Shandong University 2015WLJH44

More Information
  • This paper discusses the parameter and differentiation order identification of continuous fractional order KiBaM models in ARX (autoregressive model with exogenous inputs) and OE (output error model) forms. The least squares method is applied to the identification of nonlinear and linear parameters, in which the Grünwald-Letnikov definition and short memory principle are applied to compute the fractional order derivatives. An adaptive P-type order learning law is proposed to estimate the differentiation order iteratively and accurately. Particularly, a unique estimation result and a fast convergence speed can be arrived by using the small gain strategy, which is unidirectional and has certain advantages than some state-of-art methods. The proposed strategy can be successfully applied to the nonlinear systems with quasi-linear characteristics. The numerical simulations are shown to validate the concepts.


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