Model Predictive Synchronous Control of Barrel Temperature for Injection Molding Machine Based on Diagonal Recurrent Neural Networks

被引：13
|
作者：
Peng, Yonggang [1 ]
Wei, Wei [1 ]
Wang, Jun [2 ]
机构：
[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China
[2] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China
来源：
MATERIALS AND MANUFACTURING PROCESSES | 2012年 / 28卷 / 01期
关键词：
Barrel temperature; Diagonal recurrent neural networks; Model predictive control; Synchronous control; GENETIC ALGORITHMS; MELT TEMPERATURE; OPTIMIZATION; PARAMETERS;
D O I：
10.1080/10426914.2012.718476
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A nonlinear model predictive control (NMPC) based on diagonal recurrent neural network (DRNN) was used to control multisection barrel melt temperatures of an injection molding machine. In this method a DRNN was used to construct a nonlinear predictive model of barrel melt temperatures and genetic algorithm (GA) was used as a rolling optimization tool. Simulations and experimental results show that this method not only guarantees the accuracy of temperature control of barrel melt temperatures but also improves synchronization of barrel temperature control and it improves the consistency of the barrel melt polymer and the quality of the molded parts.
引用
收藏
页码：24 / 30
页数：7
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