Abstract
This paper considers the parameter identification of bilinear systems with unknown states, which are disturbed by an autoregressive moving average noise. The hierarchial identification principle is employed to derive new algorithms for interactively estimating the states and parameters via a bilinear state observer. However, the general bilinear state-space model involves many parameters, which causes heavy computational burden. Motivated by this fact, we propose a hierarchical generalized extended least squares (HGELS) algorithm by decomposing the original system into a series of subsystems with small dimensions for enhancing computational efficiency. The performance of the proposed algorithms is illustrated through a numerical example.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (61873111), the Qing Lan Project, the Postdoctoral Science Foundation of Jiangsu Province (No. 1701020A), the 333 Project of Jiangsu Province (No. BRA2018328).
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Xu, L., Zhang, X., Ding, F. (2020). Recursive Estimation Method for Bilinear Systems by Using the Hierarchical Identification Principle. In: Wang, R., Chen, Z., Zhang, W., Zhu, Q. (eds) Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Lecture Notes in Electrical Engineering, vol 582. Springer, Singapore. https://doi.org/10.1007/978-981-15-0474-7_92
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DOI: https://doi.org/10.1007/978-981-15-0474-7_92
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