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Recursive least squares parameter estimation algorithm for dual-rate sampled-data nonlinear systems
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DOI：10.1007/s11071-013-1212-1.png)
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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.
Keyword:
Hammerstein system
Least squares algorithm
Dual-rate sampling
Key-term separation principle
Auxiliary model identification idea
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期刊
IF:
6
论文数:
1.4W
被引数:
4.1W
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