摘要
由于传统MPCVD设备样品台温度控制需要使用手动调节样品台距等离子源的距离,且调节精度差,为此设计一种样品台温控系统,同时设计了样品台温控系统硬件控制器。该控制器以STM32F407单片机为核心,步进电机、丝杠、金属波纹管为执行机构,通过控制样品台在腔体内距等离子源的距离,实现温度控制。并提出一种将改进鲸鱼算法(WOA)与神经网络PID控制算法结合的控制方法,实现PID参数的自适应调整。仿真和实验结果表明:相对于传统PID算法,使用该温控系统进行温度控制时,超调量更小,控制精度更高,控制效果有很大提升。
As the temperature control of the sample station of traditional MPCVD equipment requires manual adjusting the displacement of the sample station from plasma source,and the adjustment accuracy is poor,a temperature control system for the sample station was designed.At the same time,a hardware controller of the temperature control system for the sample station was designed.In the controller,STM32F407 single chip microcomputer was taken as the core,stepper motor,lead screw and metal bellows were taken as the actuator.To control the displacement of the sample table from plasma source in the cavity body,temperature control was achieve.A control method combining the improved whale optimization algorithm(WOA)and neural network PID control algorithm was proposed to realize the adaptive adjustment of PID parameters.The simulation and experimental results show that,compared with the traditional PID algorithm,the overshoot is smaller,the control accuracy is higher,and the control effect is greatly improved when using the designed temperature control system.
作者
张威
任天平
ZHANG Wei;REN Tianping(School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou Henan 450001,China)
出处
《机床与液压》
北大核心
2024年第5期144-149,共6页
Machine Tool & Hydraulics
关键词
MPCVD设备
温控系统
改进鲸鱼算法
BP神经网络
MPCVD equipment
temperature control system
improved whale optimization algorithm
BP neural network
