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Recursive Least Squares Algorithm for Nonlinear Dual-rate Systems Using Missing-Output Estimation Model

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Abstract

In this paper, a recursive least squares algorithm is proposed for a class of nonlinear dual-rate systems. By using the missing-output estimation model, the unavailable outputs can be estimated. Then, the unknown parameters can be estimated from all the inputs and outputs. Compared with the polynomial transformation technique and the lifting technique, the unknown parameters can be estimated directly by using the missing-output estimation model, without increasing the number of parameters. The convergence analysis and the simulation results indicate that the proposed method is effective.

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Jing Chen, Yanjun Liu & Xuehai Wang

Authors
  1. Jing Chen
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  2. Yanjun Liu
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  3. Xuehai Wang
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Corresponding author

Correspondence to Jing Chen.

Additional information

This work was supported by the National Natural Science Foundation of China (Nos. 61403165, 61374126), the Natural Science Foundation of Jiangsu Province (No. BK20131109) and the Post Doctoral Foundation of Jiangsu Province (No. 1501015A).

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Chen, J., Liu, Y. & Wang, X. Recursive Least Squares Algorithm for Nonlinear Dual-rate Systems Using Missing-Output Estimation Model. Circuits Syst Signal Process 36, 1406–1425 (2017). https://doi.org/10.1007/s00034-016-0368-6

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