Combined state and multi-innovation parameter estimation for an input non-linear state-space system using the key term separation

In this study, the authors study the state and parameter estimation problem for an input non-linear system consisting of a static non-linear block and a linear time-invariant state space subsystem. Using the filtering technique, a filtering based multi-innovation generalised stochastic gradient (SG)...

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Vydané v:IET control theory & applications Ročník 10; číslo 13; s. 1503 - 1512
Hlavní autori: Wang, Xuehai, Ding, Feng, Hayat, Tasawar, Alsaedi, Ahmed
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: The Institution of Engineering and Technology 29.08.2016
Predmet:
Algorithms
Computer simulation
Dynamical systems
Filtering
filtering based multiinnovation generalised stochastic gradient algorithm
Filtration
gradient methods
input nonlinear system
key term separation
linear systems
linear time‐invariant state space subsystem
multiinnovation generalised SG algorithm
multiinnovation parameter estimation problem
nonlinear state‐space system
nonlinear systems
Nonlinearity
Parameter estimation
redundant parameters
Regular Papers
Separation
state estimation problem
state‐space methods
key term separation
linear systems
state estimation problem
nonlinear state-space system
multiinnovation parameter estimation problem
nonlinear systems
redundant parameters
multiinnovation generalised SG algorithm
parameter estimation
filtering based multiinnovation generalised stochastic gradient algorithm
linear time-invariant state space subsystem
gradient methods
state-space methods
input nonlinear system
ISSN:1751-8644, 1751-8652
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Popis
Shrnutí:In this study, the authors study the state and parameter estimation problem for an input non-linear system consisting of a static non-linear block and a linear time-invariant state space subsystem. Using the filtering technique, a filtering based multi-innovation generalised stochastic gradient (SG) algorithm is proposed for avoiding estimating the redundant parameters based on the key term separation technique. Compared with the multi-innovation generalised SG algorithm, the proposed algorithm has higher parameter estimation accuracy. Two simulation examples are provided to show that the proposed algorithm works well.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2015.1056