Autor: Moradi, A. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Ultimate state boundedness of underactuated spacecraft subject to an unmatched disturbance
Autorzy:: Moradi, R.
Alikhani, A.
Jegarkandi, M. F.
Powiązania:: https://bibliotekanauki.pl/articles/280303.pdf
Data publikacji:: 2017
Wydawca:: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:: underactuated spacecraft stabilization
non-matched disturbances
global uniform ultimate boundedness
Opis:: Ultimate state boundedness for underactuated spacecraft subject to large non-matched disturbances is attained. First, non-smooth time-invariant state feedback control laws that make the origin asymptotically stable are obtained. Then, the controller is extended to make the closed-loop system globally uniformly ultimately bounded under the following conditions: 1) the disturbances acting on the directly actuated states are known and 2) the disturbance acting on the unactuated state is bounded and its profile need not be known. Finally, numerical simulations are presented to verify the analytical results. A large step disturbance is considered, and it is shown that the proposed controller makes the closed-loop system globally uniformly ultimately bounded. The proposed method is rather general and can be extended to other systems.
Źródło:: Journal of Theoretical and Applied Mechanics; 2017, 55, 3; 1055-1066
1429-2955
Pojawia się w:: Journal of Theoretical and Applied Mechanics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network
Autorzy:: Najafabadi, S. S.
Anaraki, A. T.
Moradi, M.
Powiązania:: https://bibliotekanauki.pl/articles/281868.pdf
Data publikacji:: 2017
Wydawca:: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:: deep drawing
finite element analysis (FEA)
multi-layer artificial neural network (ANN)
Taguchi design
Opis:: In this paper, the deep drawing process of an automobile panel in order to select the appropriate amount of parameters has been investigated. The parameters include friction between the blank and die, blank width and length, blank thickness and gap between the blank and blank-holder. A multi-layer artificial neural network (ANN) trained by finite element analyses (FEA) is applied in order to improve forming parameters and achieve a better quality. As the FEA results are used to train the ANN, the FEA results have been verified by three experiments. Finally, an appropriate amount of each parameter is predicted by the trained ANN and a FEA has been done based on the ANN prediction to evaluate the accuracy of the trained ANN. Moreover, it is shown that the ANN could predict results within a 10 percent error. In addition, the proposed method for prediction of the appropriate parameters (ANN) is confirmed by comparing with the Taguchi design of experiment prediction. It is also shown that the model obtained by the former method has lower errors than the latter one. In this study, the Taguchi model is used to evaluate the effect of parameters on tearing and wrinkling. Based on the Taguchi design of experiment, while the blank length is the most effective parameter on tearing, the maximum height of wrinkles on flanged parts mainly depends on the blank thickness.
Źródło:: Journal of Theoretical and Applied Mechanics; 2017, 55, 2; 707-718
1429-2955
Pojawia się w:: Journal of Theoretical and Applied Mechanics
Dostawca treści:: Biblioteka Nauki

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