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Recursive least squares parameter estimation algorithm for dual-rate sampled-data nonlinear systems

Abstract

This paper focuses on the identification problem of Hammerstein systems with dual-rate sampling. Using the key-term separation principle, we derive a regression identification model with different input updating and output sampling rates. To solve the identification problem of the dual-rate Hammerstein systems with the unmeasurable variables in the information vector, an auxiliary model-based recursive least squares algorithm is presented by replacing the unmeasurable variables with their corresponding recursive estimates. Convergence properties of the algorithm are analyzed. Simulation results show that the proposed algorithm can estimate the parameters of a class of nonlinear systems.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China, the Fundamental Research Funds for the Central Universities (JUDCF11042, JUDCF12031), the PAPD of Jiangsu Higher Education Institutions and the 111 Project (B12018)

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Correspondence to Ruifeng Ding.

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Li, X., Zhou, L., Sheng, J. et al. Recursive least squares parameter estimation algorithm for dual-rate sampled-data nonlinear systems. Nonlinear Dyn 76, 1327–1334 (2014). https://doi.org/10.1007/s11071-013-1212-1

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  • DOI: https://doi.org/10.1007/s11071-013-1212-1

Keywords

  • Hammerstein system
  • Least squares algorithm
  • Dual-rate sampling
  • Key-term separation principle
  • Auxiliary model identification idea