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Bias compensation recursive algorithm for dual-rate rational models

Chen, Jing; Liu, Yanjun; Zhu, Quanmin


Jing Chen

Yanjun Liu

Quan Zhu
Professor in Control Systems


© The Institution of Engineering and Technology 2018. 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.

Journal Article Type Article
Publication Date Nov 6, 2018
Journal IET Control Theory and Applications
Print ISSN 1751-8644
Electronic ISSN 1751-8652
Publisher Institution of Engineering and Technology
Peer Reviewed Peer Reviewed
Volume 12
Issue 16
Pages 2184-2193
APA6 Citation Chen, J., Liu, Y., & Zhu, Q. (2018). Bias compensation recursive algorithm for dual-rate rational models. IET Control Theory and Applications, 12(16), 2184-2193.
Keywords system identification, rational model, parameter estimation
Publisher URL
Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available here:


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