| 基于深度神经网络的注塑过程预测控制 |
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| 引用本文： | 黎耀东,任志刚,吴宗泽.基于深度神经网络的注塑过程预测控制[J].广东工业大学学报,2022,39(5):120-126,136. |
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| 作者姓名： | 黎耀东 任志刚 吴宗泽 |
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| 作者单位： | 广东工业大学 自动化学院，广东 广州 510006 |
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| 基金项目： | 国家自然科学基金资助项目 (62073088, U1911401)；广东省重点领域研发计划项目(2021B0101200005)；广东省基础与应用基础研究基金资助项目(2019A1515011606) |
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| 摘 要： | 注塑机作为现代工业中重要的塑料件生产与制造设备，其智能化发展一直备受业界关注。伴随着航空航天、电力电子、汽车制造等行业的发展，如何实现对注塑件的高精度、高效率、绿色节能化生产，是目前注塑机的重要研制方向。本文针对注塑成型过程中采用传统模型预测控制(Model Predictive Control, MPC)难以保证跟踪控制实时性的问题，提出一种结合深度神经网络(Deep Neural Network, DNN)学习的注塑过程预测控制方法。在注塑机注射过程动力学模型基础上，创建带约束条件的模型预测控制器，对控制器运行数据进行采集并用以训练深度神经网络，实现了基于深度神经网络控制的注射速度的跟踪预测控制。仿真实验结果表明，采用本文所提出的学习预测控制策略能够有效避免注塑过程中因模型预测控制所产生的复杂计算，并满足工业实时性要求，具有应用前景。
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| 关 键 词： | 深度神经网络 模型预测控制 注塑成型 最优控制 |
| 收稿时间： | 2022-03-29 |
Deep Neural Network Based Predictive Control for Injection Molding Process |
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| Li Yao-dong,Ren Zhi-gang,Wu Zong-ze.Deep Neural Network Based Predictive Control for Injection Molding Process[J].Journal of Guangdong University of Technology,2022,39(5):120-126,136. |
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| Authors: | Li Yao-dong Ren Zhi-gang Wu Zong-ze |
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| Affiliation: | School of Automation, Guangdong University of Technology, Guangzhou 510006, China |
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| Abstract: | The development of injection molding machines has always piqued the industry's interest as an important production and manufacturing equipment for plastic parts in modern industry. How to achieve high-precision, high-efficiency, green, and energy-saving injection molding parts is an important development direction for injection molding machines as the aerospace, power electronics, automobile manufacturing, and other industries grow. A strategy that combines the deep neural network (DNN) is proposed to realize the predictive control of the injection molding process of the injection molding machine, in response to the problem that traditional model predictive control (MPC) finds it difficult to guarantee real-time tracking in the injection molding process. A model predictive controller with constraints is created based on the dynamic model of the injection molding machine injection process, and the controller's operating data is collected and used to train the deep neural network to realize the predictive control of the injection molding process based on the deep neural network control. The simulation results show that this strategy can effectively avoid the complex calculation caused by the model predictive control in the injection molding process and meet the real-time requirements and has a broad application prospect. |
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| Keywords: | deep neural network (DNN) model predictive control (MPC) injection molding process optimal control |
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