基于反向传播神经网络PID的高功率微波炉温度控制

王威; 李少甫; 吴昊; 蒋成; 唐颖颖

doi:10.11884/HPLPB202436.230280

基于反向传播神经网络PID的高功率微波炉温度控制

doi: 10.11884/HPLPB202436.230280

西南科技大学信息工程学院，四川绵阳 621010

基金项目: 国家自然科学基金委员会-中国工程物理研究院联合基金项目(U1830201)

详细信息

作者简介:
王威，1309318818@qq.com

中图分类号: TP273
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- 被引次数: 0
出版历程
- 收稿日期: 2023-08-22
- 修回日期: 2023-11-22
- 录用日期: 2023-12-01
- 网络出版日期: 2024-01-15
- 刊出日期: 2024-01-15

Research on temperature control of high power microwave oven based on back propagation neural network PID

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China

摘要

摘要: 针对现有10 kW高功率工业微波炉，采用继电器作为控制执行器，在使用传统控制方法加热时，温度存在较大超调和明显振荡，系统温度稳定性较低，为解决上述问题将反向传播神经网络PID（BPNNPID）控制引入到该装置微波加热温度控制中，并以自来水为加热对象进行仿真对比与实验验证。首先，利用现有输入输出实验数据，建立工业微波炉温度控制模型；其次，运用MATLAB/SIMULINK搭建高功率工业微波炉温度控制系统并进行仿真对比实验；最后，实验验证BPNNPID控制方法在加热5 kg自来水时工业微波炉的温度控制性能，实验结果表明，较常规PID、模糊PID控制，该方法在微波加热过程中对媒质温度控制超调更小且未发生明显温度振荡，有效改善了高功率工业微波炉工作时的系统温度稳定性，有助于提高产品质量和安全性能。
- 高功率 /
- 微波加热 /
- 反向传播神经网络 /
- PID /
- 温度控制
Abstract: For the existing 10 kW high-power industrial microwave oven, a relay is used as the control actuator. When using traditional control methods for heating, there is a large overshoot and obvious temperature oscillation, and the system temperature stability is low. To solve the above problems, back propagation neural network PID control is introduced into the microwave heating temperature control of the installation, and simulation comparison and experimental verification are conducted using tap water as the heating object. Firstly, using existing input and output experimental data, establish a temperature control model for industrial microwave ovens; Secondly, use MATLAB/SIMULINK to build a high-power industrial microwave oven temperature control system and conduct simulation comparative experiments; Finally, experimently verify the temperature control performance of the back propagation neural network PID control method in industrial microwave ovens when heating 5 kg of tap water. The experimental results show that this method has smaller overshoot and no significant temperature oscillation compared to conventional PID and fuzzy PID control in the medium temperature control during microwave heating process, effectively improving the system temperature stability during the operation of high-power industrial microwave ovens, and helping to improve product quality and safety performance.
- high power /
- microwave heating /
- back propagation neural network /
- PID /
- temperature control

HTML全文

图 1 基于BPNNPID的微波加热温度控制器结构

Figure 1. Structure of microwave heating temperature controller based on back propagation neural network PID (BPNNPID)

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图 2 BP神经网络结构图

Figure 2. BP neural network structure diagram

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图 3 微波加热过程输入输出数据

Figure 3. Input and output data of microwave heating process

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图 4 辨识模型拟合曲线

Figure 4. Identification model fitting curve

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图 5 微波加热温度控制系统仿真模型

Figure 5. Simulation model of microwave heating temperature control system

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图 6 高功率微波炉实物及结构图

Figure 6. Device and structural diagram of high power microwave oven

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图 7 微波加热温度仿真对比

Figure 7. Comparison of microwave heating temperature simulation

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图 8 微波加热温度控制系统框图

Figure 8. Block diagram of microwave heating temperature control system

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图 9 微波加热温度实验对比

Figure 9. Comparison of microwave heating temperature experiments

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表 1 各控制方法控制性能对比

Table 1. Comparison of control performance of various control methods

	simulated rise time/s	measured rise time/s	simulated peak time/s	measured peak time/s	simulated overshoot/%	measured overshoot/%	simulated maximum steady-state error/%	measured maximum steady-state error/%
PID	120	230	200	290	22.36	16	11.18	9
fuzzy PID	210	220	270	360	7.28	11.5	4.7	8
BPNNPID	320	260	330	380	0.7	6	0.7	4

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