scholarly journals A comparison of compensation methods for random input data dropouts in networked iterative learning control system

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Lixun Huang ◽  
Hanqing Ding ◽  
Zhe Zhang ◽  
Qiuwen Zhang ◽  
Lijun Sun
Author(s):  
Geng-Qun Huang ◽  
Han-Xiong Huang

An online wall thickness control strategy for the extrusion blow molded part was proposed in this work. A simulation-based optimization method combining with finite element, artificial neural network, and genetic algorithm was used to determine the initial die gap profile for a part with required thickness distribution. A multi-channel ultrasonic thickness measurement system was built up to get the in-mold wall thickness of the blow molded part. Then, a feedback closed-loop control system based on fuzzy iterative learning control algorithm was designed and implemented to control the wall thickness of blow molded part. The results showed that the online wall thickness control system developed in this work can automatically achieve a proper die gap profile and get the satisfied part thickness distribution.


2017 ◽  
Vol 40 (6) ◽  
pp. 1757-1765 ◽  
Author(s):  
Chengbin Liang ◽  
JinRong Wang

In order to track the desired reference trajectory from an oscillating control system with two delays in a finite time interval, we design iterative learning control updating laws to generate a sequence of input control functions such that the error between the output and the desired reference trajectories tends to zero via a suitable norm in the sense of uniform convergence. Here, we adopt a delayed matrix function to characterize the output state, which can be easily solved in the simulation. As a result, convergence analysis results are given. Finally, simulation results are provided to illustrate the effectiveness of the proposed controllers.


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