Accurate Position Control of a Servo-Hydraulic Test Cylinder by Iterative Learning Control Technique

2014 ◽  
Author(s):  
Lingjun Li ◽  
Uwe Poms ◽  
Thomas Thurner
Keyword(s):  
Iterative Learning Control ◽  
Position Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Technique ◽  
Hydraulic Test ◽  
Test Cylinder ◽  
Accurate Position

Research on Synchronous Control Technique of the Tube Hydroforming

2012 ◽  
Vol 233 ◽  
pp. 142-145
Author(s):  
Jin Yu ◽  
Deng Xu ◽  
Guo Qing Huang
Keyword(s):  
Control Strategy ◽  
Iterative Learning Control ◽  
Tube Hydroforming ◽  
Great Influence ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Technique ◽  
Important Indicator ◽  
Synchronous Control ◽  
Control Accuracy

Synchronous control accuracy of side-cylinders of the hydroforming press is a very important indicator , it has a great influence on the quality of products. Generally, people take PID control strategy to improve the precision of hydroforming press. In this paper, a mathematical model of the side-cylinders’ hydraulic system is established and the PID and iterative learning control strategy is used, respectively, to find which one is better . The results show that the iterative learning control strategy has a higher synchronous control accuracy.

A Position Control Scheme Using High-Order Iterative Learning Control for Permanent Magnet Linear Motor

2012 ◽  
Vol 546-547 ◽  
pp. 236-241
Author(s):  
Song Yang ◽  
Hang Ma ◽  
Jun You Yang ◽  
Huai Yang Shen ◽  
Sheng Quan Chang
Keyword(s):  
Permanent Magnet ◽  
Iterative Learning Control ◽  
Position Control ◽  
Learning Control ◽  
Linear Motor ◽  
Iterative Learning ◽  
High Order ◽  
State Uncertainty ◽  
Permanent Magnet Linear Motor ◽  
Point To Point

A high-order Iterative Learning Control (ILC) strategy is designed for Permanent Magnet Linear Motor (PMLSM). This iterative learning controller is designed to high-order open and closed loop PID-type. This paper gives the convergence conditions of the proposed controller and proves that the tracking bound is determined by the bounds of state uncertainty and output disturbance to the system. The proposed ILC strategy result shows that position precision of PMLSM in point-to-point motion can be effectively improved with this scheme. The experiments demonstrate that the presented ILC strategy is effective and robust.

scholarly journals Trajectory Tracking between Josephson Junction and Classical Chaotic System via Iterative Learning Control

2018 ◽  
Vol 8 (8) ◽  
pp. 1285 ◽  
Author(s):  
Chun-Kai Cheng ◽  
Paul Chao
Keyword(s):  
Josephson Junction ◽  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Tracking System ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Technique ◽  
Rossler System ◽  
Rössler System ◽  
Two Stages

This article addresses trajectory tracking between two non-identical systems with chaotic properties. To study trajectory tracking, we used the Rossler chaotic and resistive-capacitive-inductance shunted Josephson junction (RCLs-JJ) model in a similar phase space. In order to achieve goal tracking, two stages were required to approximate target tracking. The first stage utilizes the active control technique to transfer the output signal from the RCLs-JJ system into a quasi-Rossler system. Next, the RCLs-JJ system employs the proposed iterative learning control scheme in which the control signals are from the drive system to trace the trajectory of the Rossler system. The numerical results demonstrate the validity of the proposed method and the tracking system is asymptotically stable.

On the motion/stiffness decoupling property of articulated soft robots with application to model-free torque iterative learning control

2020 ◽  
pp. 027836492094327
Author(s):  
Riccardo Mengacci ◽  
Franco Angelini ◽  
Manuel G Catalano ◽  
Giorgio Grioli ◽  
Antonio Bicchi ◽  
...  
Keyword(s):  
Iterative Learning Control ◽  
Position Control ◽  
Variable Stiffness ◽  
Learning Control ◽  
Iterative Learning ◽  
Soft Robots ◽  
Input Action ◽  
Model Free ◽  
Stiffness Profile ◽  
Anticipatory Action

This article tackles the problem of controlling articulated soft robots (ASRs), i.e., robots with either fixed or variable elasticity lumped at the joints. Classic control schemes rely on high-authority feedback actions, which have the drawback of altering the desired robot softness. The problem of accurate control of ASRs, without altering their inherent stiffness, is particularly challenging because of their complex and hard-to-model nonlinear dynamics. Leveraging a learned anticipatory action, iterative learning control (ILC) strategies do not suffer from these issues. Recently, ILC was adopted to perform position control of ASRs. However, the limitation of position-based ILC in controlling variable stiffness robots is that whenever the robot stiffness profile is changed, a different input action has to be learned. Our first contribution is to identify a wide class of ASRs, whose motion and stiffness adjusting dynamics can be proved to be decoupled. This class is described by two properties that we define: strong elastic coupling, relative to motors and links of the system and their connections; and homogeneity, relative to the characteristics of the motors. Furthermore, we design a torque-based ILC scheme that, starting from a rough estimation of the system parameters, refines the torque needed for the joint positions tracking. The resulting control scheme requires minimum knowledge of the system. Experiments on variable stiffness robots prove that the method effectively generalizes the iterative procedure with respect to the desired stiffness profile and allows good tracking performance. Finally, potential restrictions of the method, e.g., caused by friction phenomena, are discussed.

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