Iterative learning control with sampled-data feedback for robot manipulators

Archives of Control Sciences

Kawasaki Haruhisa¹  Mouri Tetsuya¹  Boiadjiev George²  Delchev Kamen³ 

[1] Department of Human and Information Systems, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan;Faculty of Mathematics and Informatics, Sofia University;Institute of Mechanics, Bulgarian Academy of Sciences, "Acad.G.Bonchev" Str., bl.4, BG-1113 Sofia;

关键词: sampled-data systems;    iterative learning control;    robot manipulators;    convergence analysis;   

DOI  :  10.2478/acsc-2014-0018

来源: DOAJ

【 摘 要 】

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

【 授权许可】

Unknown