Temat: iterative identification - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Parameter estimation of MIMO two-dimensional ARMAX model based on IGLS method
Autorzy:: Hayati, Zohreh
Shafieirad, Mohsen
Zamani, Iman
Amiri Mehra, Amir Hossein
Abbasi, Zohreh
Powiązania:: https://bibliotekanauki.pl/articles/2183469.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:: ARMAX model
discrete 2D systems
iterative identification
MIMO system
parameter estimation
Opis:: This paper presents an iterative method for the unbiased identification of linearMultiple-InputMultiple-Output (MIMO) discrete two-dimensional (2D) systems. The system discussed here has Auto-Regressive Moving-Average model with exogenous inputs (ARMAX model). The proposed algorithm functions on the basis of the traditional Iterative Generalized Least Squares (IGLS) method. In summary, this paper proposes a two-dimensional Multiple-Input Multiple-Output Iterative Generalized Least Squares (2DMIGLS) algorithm to estimate the unknown parameters of the ARMAX model. Finally, simulation results show the efficiency and accuracy of the presented algorithm in estimating the unknown parameters of the model in the presence of colored noise.
Źródło:: Control and Cybernetics; 2021, 50, 3; 303--322
0324-8569
Pojawia się w:: Control and Cybernetics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: System Identification from Multiple-Trial Data Corrupted by Non-Repeating Periodic Disturbances
Autorzy:: Phan, M. Q.
Longman, R. W.
Lee, S. C.
Lee, J. W.
Powiązania:: https://bibliotekanauki.pl/articles/908209.pdf
Data publikacji:: 2003
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: automatyka
robotyka
system identification
disturbance identification
iterative learning control
repetitive control
interaction matrix
Opis:: Iterative learning and repetitive control aim to eliminate the effect of unwanted disturbances over repeated trials or cycles. The disturbance-free system model, if known, can be used in a model-based iterative learning or repetitive control system to eliminate the unwanted disturbances. In the case of periodic disturbances, although the unknown disturbance frequencies may be the same from trial to trial, the disturbance amplitudes, phases, and biases do not necessarily repeat. Furthermore, the system may not return to the same initial state at the end of each trial before starting the next trial. In spite of these constraints, this paper shows how to identify the system disturbance-free dynamics from disturbance-corrupted input-output data collected over multiple trials without having to measure the disturbances directly. The system disturbance-free model can then be used to identify the disturbances as well, for use in learning or repetitive control. This paper represents the first extension of the interaction matrix approach to the multiple-trial environment of iterative learning control.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2003, 13, 2; 185-192
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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