Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

Summary This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hi...

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Veröffentlicht in:International journal of adaptive control and signal processing Jg. 37; H. 7; S. 1827 - 1846
Hauptverfasser: Bi, Yiqun, Ji, Yan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.07.2023
Schlagworte:
Algorithms
Extended Kalman filter
extended Kalman filtering
forgetting factor
fractional‐order system
hierarchical identification
Parameter estimation
Parameter identification
State estimation
stochastic gradient algorithm
ISSN:0890-6327, 1099-1115
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Zusammenfassung:Summary This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect.
Bibliographie:ObjectType-Article-1
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ISSN:0890-6327
1099-1115
DOI:10.1002/acs.3602