Two-dimensional Model Predictive Iterative Learning Control Scheme Based on a Two-dimensional Performance Model

2014 ◽  
Vol 39 (5) ◽  
pp. 565-573 ◽  
Author(s):  
Jia SHI ◽  
Qing-Yin JIANG ◽  
Zhi-Kai CAO ◽  
Hua ZHOU ◽  
Fu-Rong GAO
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Performance Model ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Control Scheme ◽  
Two Dimensional Model

scholarly journals Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

2019 ◽  
Vol 292 ◽  
pp. 01010
Author(s):  
Mihailo Lazarević ◽  
Nikola Živković ◽  
Darko Radojević
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Closed Loop ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Control Law ◽  
Exact Linearization ◽  
Outer Loop ◽  
Control Scheme

The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.

Reference adjustment for a high-acceleration and high-precision platform via A-type of iterative learning control

2007 ◽  
Vol 221 (5) ◽  
pp. 781-789 ◽  
Author(s):  
J. H. Wu ◽  
H Ding
Keyword(s):  
High Precision ◽  
Iterative Learning Control ◽  
Control Structure ◽  
Learning Control ◽  
Iterative Learning ◽  
High Acceleration ◽  
Control Scheme ◽  
Point Motion ◽  
Point To Point ◽  
P Type

This paper studies the repetitive motion control of a high-acceleration and high-precision platform driven by linear motors. The control scheme comprises an anticipatory iterative learning control (A-ILC) component and a cascaded control structure including an inner-loop velocity PI controller and an outer-loop position P controller. During the motion process, the cascaded controller remains invariant while the A-ILC adjusts the reference command cycle by cycle to achieve better performance. Experiments are carried out to validate the proposed control structure. The results confirm that the proposed control scheme can improve the system performance significantly in both low-speed trajectory tracking motions and fast point-to-point motion. In the experiments, P-type and D-type ILCs are also utilized to adjust the reference command. Compared with the A type, P-type ILC leads to larger tracking error bounds and D-type ILC lacks a fast convergence rate for low-speed motions, while for fast point-to-point motion these two types of ILC are unable to work well.

Adaptive robust iterative learning control with application to a Delta robot

2020 ◽  
pp. 095965182093853
Author(s):  
Chems Eddine Boudjedir ◽  
Djamel Boukhetala
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
External Disturbances ◽  
Repetitive Tasks ◽  
Control Scheme ◽  
Derivative Type ◽  
Delta Robot ◽  
Proportional Derivative Controller

In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.

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