Abstract
In this paper, in order to solve the problem that the controlled object is complex or nonlinear in many times, a weighted recursive least square scheme is used to estimate the parameters of the nonlinear Wiener-Hammerstein systems with the dead zone. First of all, we make the unknown dead zone linear by the switching operator and the intermediate function, and construct the parameter identification model of Wiener-Hammerstein system. Secondly, we obtain the parametric regression model of the concerned systems for parameter identification using the key-term separation principle. Thirdly, we build a fictitious auxiliary model to replace the immeasurable intermediate variable. And then, we estimate the parameters of the obtained model with the fictitious auxiliary model using the weighted recursive least square. Finally, we verify the feasibility of the algorithm by MATLAB simulation.
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Acknowledgments
Our work is supported by Grant No.61973036, 61433003, from National Natural Science Foundation (NNSF) of China.
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Lan, R., Ren, X., Li, L. (2022). Weighted Recursive Least Square for Parameter Identification of Nonlinear Wiener–Hammerstein Systems. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 803. Springer, Singapore. https://doi.org/10.1007/978-981-16-6328-4_47
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DOI: https://doi.org/10.1007/978-981-16-6328-4_47
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