scholarly journals On the current error based sampled-data iterative learning control with reduced memory capacity

2015 ◽  
Vol 12 (3) ◽  
pp. 307-315 ◽  
Author(s):  
Chiang-Ju Chien ◽  
Yu-Chung Hung ◽  
Rong-Hu Chi
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Memory Capacity ◽  
Iterative Learning ◽  
Sampled Data ◽  
Current Error

An improvement on the transient response of tracking for the sampled-data system based on an improved PD-type iterative learning control

2014 ◽  
Vol 351 (2) ◽  
pp. 1130-1150 ◽  
Author(s):  
Fu-Ming Chen ◽  
Jason Sheng-Hong Tsai ◽  
Ying-Ting Liao ◽  
Shu-Mei Guo ◽  
Ming-Chung Ho ◽  
...  
Keyword(s):  
Transient Response ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Data System ◽  
Sampled Data

scholarly journals Iterative learning control with sampled-data feedback for robot manipulators

2014 ◽  
Vol 24 (3) ◽  
pp. 299-319 ◽  
Author(s):  
Kamen Delchev ◽  
George Boiadjiev ◽  
Haruhisa Kawasaki ◽  
Tetsuya Mouri
Keyword(s):  
Feedback Control ◽  
Iterative Learning Control ◽  
Feedforward Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Sampled Data ◽  
Model Based ◽  
Data Feedback

Abstract 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

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