Self-adaptive temperature control of vacuum furnace based on single neuron PID

doi:10.13251/j.issn.0254-6051.2020.12.046

Abstract

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.

Key words: vacuum heat treatment, PID control, single neuron, simulation, temperature control system

CLC Number:

TP273

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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