Bias compensation recursive algorithm for dual-rate rational models

In dual-rate rational systems, some output data are missing (unmeasurable) to make the traditional recursive least squares (RLS) parameter estimation algorithms invalid. In order to overcome this difficulty, this study develops a bias compensation RLS algorithm for estimating the missing outputs and...

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Veröffentlicht in:IET control theory & applications Jg. 12; H. 16; S. 2184 - 2193
Hauptverfasser: Chen, Jing, Liu, Yanjun, Zhu, Quanmin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 06.11.2018
Schlagworte:
bias compensation recursive algorithm
bias compensation RLS algorithm
correlated noise
dual‐rate rational systems
least squares approximations
particle filter
particle filtering (numerical methods)
recursive estimation
recursive least squares parameter estimation algorithms
Research Article
unbiased parameter estimation
recursive least squares parameter estimation algorithms
least squares approximations
dual-rate rational systems
recursive estimation
particle filtering (numerical methods)
unbiased parameter estimation
bias compensation recursive algorithm
particle filter
bias compensation RLS algorithm
correlated noise
ISSN:1751-8644, 1751-8652
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Zusammenfassung:In dual-rate rational systems, some output data are missing (unmeasurable) to make the traditional recursive least squares (RLS) parameter estimation algorithms invalid. In order to overcome this difficulty, this study develops a bias compensation RLS algorithm for estimating the missing outputs and then the model parameters. The algorithm based on auxiliary model and particle filter has four steps: (i) to establish an auxiliary model to estimate unmeasurable outputs, (ii) to compensate bias induced by correlated noise, (iii) to add a filter to improve estimation accuracy of the unmeasurable outputs and (iv) to obtain an unbiased parameter estimation. Three examples are selected for simulation demonstrations to give further guarantees on the usefulness of the proposed algorithms. The comparative studies show that the bias compensation RLS is more effective for such systems with dual-rate input and output data.
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2018.5368