Research on Temperature Control of Cold Storage based on bp Neural Network PID Control

With the rapid development of cold chain logistics, cold storage, as its core component, the accuracy and stability of its temperature control is very important to ensure the quality of stored goods. Although the traditional PID control method is widely used, it is often difficult to achieve the ideal control effect when facing the system with large lag and nonlinear characteristics such as cold storage. In this regard, this paper will deeply discuss the matching mode of artificial neural network algorithm and PID system, and put forward a set of design scheme of cold storage temperature controller based on BP neural network PID to realize intelligent control of cold storage environment temperature. Practice has proved that in the BP neural network model, the number of nodes in the input layer corresponds to the number of temperature sensors, and the number of nodes in the output layer corresponds to the three parameters $(K_{p}, \boldsymbol{K}_{i}, \boldsymbol{K}_{d})$ of the PID controller. The number of hidden layer nodes can be selected and adjusted according to the actual situation. The BP neural network is trained by training data set, so that it can approximate the nonlinear characteristics of the controlled object, and output appropriate PID parameters, so as to realize the adaptive adjustment of parameters and improve the stability and accuracy of the system.