Recursive parameter estimation and its convergence for bilinear systems
By Xiao Zhang, Feng Ding. IET Control Theory & Applications, 2020
66 citations recorded by the source corpus.
This study develops an adaptive filtering-based recursive identification algorithm for joint estimation of states and parameters of bilinear state-space systems with an autoregressive moving average noise. In order to handle the correlated noise and unmeasurable states in parameter estimation, an adaptive filter is established to whiten the coloured noise and a bilinear state observer is constructed to update the unavailable states recursively. Then a hierarchical generalised extended least squares (HGELS) algorithm and an adaptive filtering-based HGELS algorithm are developed for simultaneously estimating the unknown states and parameters. The convergence analysis indicates that the parameter estimates can converge to their true values. A numerical example illustrates the convergence results.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/c0f6a4536e44b53a5bbf0c724e7f4793a79a25e6
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In Control and Systems Engineering
Citations in the corpus
- Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output‐error systems (2021, unknown): “Applying the matrix inversion lemma to Equations (14), (16), and (18) gives P1(t) = P1(t − 1) − P1(t − 1)ξz(t)ξz (t)P1(t − 1) 1 + ξz (t)P1(t − 1)ξz(t) , (19)”
- Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems (2020, methodology): “From the dual-rate identification models in (40) and (41), we have”
- Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation (2021, unknown): “?̂?l(k) = [?̂?l (k), b̂ T l (k), ĥ T l (k)]T, (55)”