- Tytuł:
- Iterative learning control with sampled-data feedback for robot manipulators
- Autorzy:
-
Delchev, K.
Boiadjiev, G.
Kawasaki, H.
Mouri, T. - Powiązania:
- https://bibliotekanauki.pl/articles/229323.pdf
- Data publikacji:
- 2014
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
sampled-data systems
iterative learning control
robot manipulators
convergence analysis - Opis:
- This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached.
- Źródło:
-
Archives of Control Sciences; 2014, 24, 3; 299-319
1230-2384 - Pojawia się w:
- Archives of Control Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł