摘要
为解决食品加工恒温控制系统所存在的非线性、滞后性、精度不高差问题,结合RBF神经网络和PID控制方法设计了一种智能恒温控制系统。RBF神经网络运算量小、收敛速度快,可以有效地提高系统响应速度。利用RBF神经网络的自适应能力,可实时在线调整PID控制器参数,使其达到最优。给出了控制系统结构,包括控制器、温度和湿度传感器、显示电路、鼓风电动机、排湿电动机等。最后进行了试验研究。试验结果表明:采用所述RBF-PID控制算法,烤箱温度波动明显变小;温度偏差绝对值最大只有0.8℃,偏差绝对值平均值约为0.36℃;能够明显提高温度控制精度。
An intelligent thermostatic control system was designed based on RBF neural network and PID control method to solve the problems of non-linearity, hysteretic and low precision existing in the thermostatic control system of food processing. RBF neural network had the advantages of small computation and fast convergence, which could effectively improve the response speed of the system. Using the adaptive ability of RBF neural network, the parameters of PID controller was adjusted in real time and online to reach the optimal value. The control system structure was given, including controller, temperature and humidity sensor, display circuit, wind turbine motor, moisture motor and so on. Finally, an experimental study was carried out. The experimental results showed that the temperature fluctuation of oven decreases obviously with the RBF-PID control algorithm. The maximum absolute deviation of temperature was only 0.8 ℃, and the average absolute deviation was about 0.36 ℃. It could obviously improve the temperature control accuracy.
作者
王萌
WANG Meng(Henan Institute of Economics and Trade,Zhengzhou 450003)
出处
《食品工业》
CAS
北大核心
2019年第12期219-222,共4页
The Food Industry
关键词
食品加工
神经网络
恒温控制
PID
food processing
neural network
thermostatic control
PID
