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Least squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary model

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Abstract

For the difficulty that the information vector in the identification model contains the unknown variables, we substitute these unknown variables with the outputs of the auxiliary model and then develop an auxiliary model based recursive least squares algorithm, an auxiliary model based least squares iterative (AM-LSI) algorithm, and derive an equivalent matrix decomposition based AM-LSI algorithm for input nonlinear controlled autoregressive systems based on the auxiliary model. The simulation results show that the proposed algorithms can estimate the parameters of a class of input nonlinear systems.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61273194), the Natural Science Foundation of Jiangsu Province (China, BK2012549), the CXLX13-738 Project, the JUDCF13035 Project, the PAPD of Jiangsu Higher Education Institutions, the 111 Project (B12018) and the Fundamental Research Funds for the Central Universities (No. JUSRP51322B).

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  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, P.R. China

    Huiyi Hu

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, P.R. China

    Rui Ding

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  1. Huiyi Hu
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  2. Rui Ding
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Correspondence to Rui Ding.

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Hu, H., Ding, R. Least squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary model. Nonlinear Dyn 76, 777–784 (2014). https://doi.org/10.1007/s11071-013-1168-1

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