Control of Wafer Temperature Uniformity in Rapid Thermal Processing Using an Optimal Iterative Learning Control Technique

2001 ◽  
Vol 40 (7) ◽  
pp. 1661-1672 ◽  
Author(s):  
Kwang S. Lee ◽  
Jinho Lee ◽  
Insik Chin ◽  
Jinhoon Choi ◽  
Jay H. Lee
Keyword(s):  
Thermal Processing ◽  
Iterative Learning Control ◽  
Rapid Thermal Processing ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Technique ◽  
Temperature Uniformity ◽  
Wafer Temperature

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.

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.

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