Optimal PID Parameter Tuning Method of First-Order Time-Lag System Based on BP Neural Network

In automated industrial production, the tuning method of PID parameters is proposed to consider first-order time-lag system variability and environmental uncertainties. To address this problem, this paper proposes an innovative method to optimize the PID parameter tuning. The established tuning optimization model of a first-order time-lag system to analyze the robustness of the system. Meanwhile, the solving parameter process of PID control is combined with the specificity of a multilayer feed-forward neural network based on the back-propagation algorithm, which can greatly enhance the system's efficiency and adaptability. Compared with traditional tuning methods, simulation results demonstrate that the parameter response time of a PID controller for the first-order time-lag system is significantly enhanced by the proposed method, which significantly enhances robustness against disturbances and uncertainties.