PID Parameters Auto-Tuning Method for Industrial Temperature Adjustment

The traditional BP network is the most typical and widely used artificial neural network, which easily falls into local minimum with the slow convergence speed, and other shortcomings, seriously affecting its performance and application. In this paper, considering the error signal features in PID(Proportional Integral Derivative) parameters tuning, the variable DNN (Dynamic Neural Network) structure is introduced to optimize the traditional BP network as a new strategy, especially for some time-delay systems. The simulation experiments are realized by simulation module in MATLAB. The result shows that the performance based on the designed BP-DNN neural network is better than that of traditional BPNN (Backpropagation Neural Network), with the faster convergence speed and fewer oscillations. Further experiments suggest that the parameter tuning algorithm for PID parameters based on the variable network structure is also better, presenting characteristics-faster response, higher steady-state accuracy, stronger robustness, etc.