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改进GWO的小波神经网络温控系统设计
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河南省高等学校重点科研项目(19B460012)


Design of Temperature Control System Based on Improved GWO Wavelet Neural Network
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    摘要:

    针对目前铸件砂芯表干炉温度控制性能差、燃烧效率低,设计一种新型热风循环温控系统。该系统以变限幅双交叉燃烧策略为基础,采用改进灰狼优化(GWO)算法的小波神经网络对PID控制参数进行自适应调整。系统仿真表明:与传统PID控制相比,超调量接近于0,系统调节时间减少了50%,温度切换控制速度提高了47%。最后通过砂芯烘干试验验证,与传统比值串级PID控制相比,变限幅双交叉燃烧策略和改进GWO小波神经网络PID对炉温的控制效果有很大的提升。

    Abstract:

    Aiming at the poor temperature control performance and low combustion efficiency of casting sand core surface drying furnace,a new hot air circulation temperature control system was designed.Based on the variable limiting amplitude double cross combustion strategy,the wavelet neural network with improved gray wolf optimization (GWO) algorithm was used to adaptively adjust the PID control parameters.The system simulation shows that compared with the traditional PID control,the overshoot is close to 0,the system regulation time is reduced by 50%,and the temperature switching control speed is increased by 47%.Finally,through the sand core drying test,compared with the traditional ratio cascade PID control,the variable limiting amplitude double cross combustion strategy and the improved GWO wavelet neural network PID have a great improvement on the control effect of furnace temperature.

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陈珂乐,任天平,郭帅,李保强.改进GWO的小波神经网络温控系统设计[J].机床与液压,2023,51(9):97-102.
CHEN Kele, REN Tianping, GUO Shuai, LI Baoqiang. Design of Temperature Control System Based on Improved GWO Wavelet Neural Network[J]. Machine Tool & Hydraulics,2023,51(9):97-102

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  • 在线发布日期: 2023-05-29
  • 出版日期: 2023-05-15