Design strategy for optimal iterative learning control applied on a deep drawing process

详细信息    查看全文

• 作者：B. Endelt
• 关键词：Machine learning ; Iterative learning control ; In ; process control ; Feedback control ; Metal forming ; Deep drawing ; Finite element method ; Process robustness
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：88
• 期：1-4
• 页码：3-18
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design;
• 出版者：Springer London
• ISSN：1433-3015
• 卷排序：88

文摘

Metal forming processes in general can be characterised as repetitive processes; this work will take advantage of this characteristic by developing an algorithm or control system which transfers process information from part to part, reducing the impact of repetitive uncertainties, e.g. a gradual changes in the material properties. The process is highly non-linear and the system plant is modelled using a non-linear finite element and the gain factors for the iterative learning controller is identified solving a non-linear optimal control problem. The optimal control problem is formulated as a non-linear least square problem where the system response is evaluated using a non-linear finite element model of the process.