An Iterative Learning Approach to Identify Fractional Order KiBaM Model

作者：Zhao, Yang^*; Li, Yan; Zhou, Fengyu; Zhou, Zhongkai; Chen, YangQuan

来源：IEEE/CAA Journal of Automatica Sinica, 2017, 4(2): 322-331.

DOI：10.1109/JAS.2017.7510358

• Fractional calculus
• iterative learning identification
• KiBaM model
• system identification

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 Grunwald-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.

• 出版日期2017-4
• 单位山东大学