| 基于单神经元PID的真空炉自适应温度控制 |
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| 引用本文： | 凡占稳,单琼飞,尹承锟,杨广文,王赫,丛培武.基于单神经元PID的真空炉自适应温度控制[J].金属热处理,2020,45(12):237-241. |
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| 作者姓名： | 凡占稳 单琼飞 尹承锟 杨广文 王赫 丛培武 |
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| 作者单位： | 1.北京机电研究所有限公司, 北京 100083;2.洛阳轴承研究所有限公司, 河南 洛阳 471003 |
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| 摘 要： | 根据真空热处理系统的特点,将单神经元PID控制算法应用到真空热处理系统的温度控制上。根据神经网络的非线性逼近能力和自学习自适应的特点,将单神经元网络与PID控制结合实现对真空炉温度的控制,以达到提高真空炉温度控制品质的目的。并通过计算机仿真软件进行仿真试验,仿真结果表明单神经元PID控制系统可以对控制参数自整定,其对温度控制更加稳健,具有更强的抗干扰能力和鲁棒性。经过搭建真空炉温度控制系统试验平台验证后发现,应用单神经元PID控制的真空炉系统的温升过程表现出了良好的稳定性,但是温度控制的响应速度和保温的精度略有下降。要想进一步提高温控品质,需要就单神经元PID控制方法在响应速度和控制精度上做进一步改进。
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| 关 键 词： | 真空热处理 PID控制 单神经元 仿真 温控系统 |
| 收稿时间： | 2020-08-23 |
Self-adaptive temperature control of vacuum furnace based on single neuron PID |
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| Fan Zhanwen,Shan Qiongfei,Yin Chengkun,Yang Guangwen,Wang He,Cong Peiwu.Self-adaptive temperature control of vacuum furnace based on single neuron PID[J].Heat Treatment of Metals,2020,45(12):237-241. |
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| Authors: | Fan Zhanwen Shan Qiongfei Yin Chengkun Yang Guangwen Wang He Cong Peiwu |
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| Affiliation: | 1. Beijing Research Institute of Mechanical & Electrical Technology, Beijing 100083, China;2. Luoyang Bearing Research Institute Co., Ltd., Luoyang Henan 471003, China |
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| Abstract: | According to the characteristics of vacuum heat treatment system, the single neuron PID control algorithm was applied to the temperature control of vacuum heat treatment system. Combined with the nonlinear approximation ability of neural network and the characteristics of self-learning and self-adaptive, the single neural network was combined with PID control to realize the control of vacuum furnace temperature, so as to improve the quality of vacuum furnace temperature control. The simulation results show that the single neuron network PID control system can self-tuning the control parameters, it is more robust to temperature control, has stronger anti-interference ability and robustness. After building the experimental platform of vacuum furnace temperature control system, it is found that the temperature rise process of vacuum furnace system with single neuron PID control shows good stability, but the response speed of temperature control and the accuracy of heat preservation slightly decrease. In order to further improve the quality of temperature control, the single neuron PID control method needs to be further improved in response speed and control accuracy. |
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| Keywords: | vacuum heat treatment PID control single neuron simulation temperature control system |
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