Least squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary model

For the difficulty that the information vector in the identification model contains the unknown variables, we substitute these unknown variables with the outputs of the auxiliary model and then develop an auxiliary model based recursive least squares algorithm, an auxiliary model based least squares...

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Veröffentlicht in:Nonlinear dynamics Jg. 76; H. 1; S. 777 - 784
Hauptverfasser: Hu, Huiyi, Ding, Rui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.04.2014
Springer Nature B.V
Schlagworte:
Algorithms
Automotive Engineering
Classical Mechanics
Computer simulation
Control
Dynamical Systems
Economic models
Engineering
Equivalence
Estimating techniques
Iterative methods
Least squares
Least squares method
Mathematical models
Mechanical Engineering
Monte Carlo simulation
Nonlinear control
Nonlinear dynamics
Nonlinear systems
Nonlinearity
Original Paper
Parameter estimation
Vibration
Recursive identification
Iterative method
Parameter estimation
Auxiliary model
Input nonlinear system
Least squares
ISSN:0924-090X, 1573-269X
Online-Zugang:Volltext
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Zusammenfassung:For the difficulty that the information vector in the identification model contains the unknown variables, we substitute these unknown variables with the outputs of the auxiliary model and then develop an auxiliary model based recursive least squares algorithm, an auxiliary model based least squares iterative (AM-LSI) algorithm, and derive an equivalent matrix decomposition based AM-LSI algorithm for input nonlinear controlled autoregressive systems based on the auxiliary model. The simulation results show that the proposed algorithms can estimate the parameters of a class of input nonlinear systems.
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-013-1168-1