Least squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary model |
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Authors: | Huiyi Hu Rui Ding |
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Affiliation: | 1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, P.R. China 2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, P.R. China
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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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