Cooperative Distributed Predictive Control for Smart Injection Molding Systems With One-Tap Memory

This article examines for the first time an integrated structure of smart injection molding systems (IMS) based on Industry 4.0 technologies and provides a system-level solution for manufacturing smart products. The fully automated smart IMS structure allows manufacturers to produce thermoplastic pr...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2024-06, Vol.20 (6), p.8850-8860
Main Authors: Zhou, Yuanqiang, Hu, Huanjia, Ding, Weilong, Gao, Kaihua, Li, Dewei, Gao, Furong
Format: Article
Language:English
Subjects:
Algorithms
Closed loops
Control methods
Cooperative control
Distributed control
Industrial applications
Industry 4.0
Injection molding
injection molding system (IMS)
Manifolds
Manipulator dynamics
Manipulators
Memory tasks
model predictive control
Multiple robots
Optimization
Performance indices
Predictive control
process systems engineering
Raw materials
Robot arms
Robot kinematics
Robots
Service robots
Task analysis
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Summary:This article examines for the first time an integrated structure of smart injection molding systems (IMS) based on Industry 4.0 technologies and provides a system-level solution for manufacturing smart products. The fully automated smart IMS structure allows manufacturers to produce thermoplastic products directly from raw materials without requiring any human labor. Following this, we focus on the control problem associated with the auxiliary robot manipulators that support the smart IMS. A cooperative distributed predictive control (DPC) algorithm with one-tap memory is proposed to achieve optimal closed-loop performance for multiple robot manipulators simultaneously performing their respective tasks. Using one-tap memory in smart IMS, we optimize the local performance index, which includes penalized terms diverging from it, and the global cooperation effort with limited memory acceleration to reach manifold consensus, without requiring manipulators to exchange information iteratively at each step. The cooperative DPC algorithm is also applied to five robot manipulators in the smart IMS, which require them to work cooperatively to achieve rhythmic and synchronized movements. Industrial experiment results demonstrate the feasibility of smart IMS combined with the cooperative DPC. Moreover, the cooperative DPC method outperforms the other two predictive control methods based on three metrics of the smart IMS.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3378838