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- Title
Parameter estimation for a multi‐input multi‐output state‐space system with unmeasurable states through the data filtering technique.
- Authors
Cui, Ting; Ding, Feng; Sheng, Jie
- Abstract
Parameter estimation is an important tool for modelling a real system. This study considers the parameter estimation problem of a multi‐input multi‐output state‐space system with unmeasurable states. By employing the negative gradient search and cutting down redundant parameter estimates, the authors derive a partially‐coupled generalised stochastic gradient (PC‐GSG) algorithm to estimate the parameters. Considering the unmeasurable states, they present a new state observer which replaces the unknown parameters with their estimates to generate state estimates. By combining the PC‐GSG algorithm and the new state observer, they obtain a state observer based partially‐coupled generalised stochastic gradient (SO‐PC‐GSG) algorithm to estimate the parameters and states. In order to eliminate the interference of the coloured noise and strengthen the performance of the SO‐PC‐GSG algorithm, they propose a state observer based filtering PC‐GSG algorithm by means of the data filtering technique. Finally, the effectiveness of the proposed algorithms is investigated in a simulation study.
- Publication
IET Control Theory & Applications (Wiley-Blackwell), 2020, Vol 14, Issue 19, p3062
- ISSN
1751-8644
- Publication type
Article
- DOI
10.1049/iet-cta.2020.0866