Journal
IET CONTROL THEORY AND APPLICATIONS
Volume 11, Issue 3, Pages 390-400Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-cta.2016.0491
Keywords
linear systems; recursive estimation; least squares approximations; time-varying systems; time-varying parameters; interval-varying RLS algorithm; decomposition-based RLS algorithm; auxiliary model-based RLS algorithm; auxiliary model identification; recursive least squares estimation; parameter identification problems; pseudolinear systems; parameter estimation
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Funding
- National Natural Science Foundation of China [61273194]
- 111 Project [B12018]
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This study focuses on the parameter identification problems of pseudo-linear systems. The main goal is to present recursive least squares (RLS) estimation methods based on the auxiliary model identification idea and the decomposition technique. First, an auxiliary model-based RLS algorithm is given as a comparison. Second, to improve the computation efficiency, a decomposition-based RLS algorithm is presented. Then for the system identification with missing data, an interval-varying RLS algorithm is derived for estimating the system parameters. Furthermore, this study uses the decomposition technique to reduce the computational cost in the interval-varying RLS algorithm and introduces the forgetting factors to track the time-varying parameters. The simulation results show that the proposed algorithms can work well.
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