Design and application of PLC control system based on neural network

In order to better break through the performance bottleneck of traditional PLC in nonlinear temperature control system, this study proposes an incremental PID control algorithm based on BP neural network optimization. By constructing a dual model architecture of transfer function and deep learning, a 3-9-3 topological neural network is designed to dynamically adjust PID parameters to achieve collaborative optimization of proportion, integral and differential coefficients. The experimental system uses Siemens S7-1214 PLC to build a four-level control architecture, integrates TIA Portal development environment and modular programming strategy, and realizes the whole process without fluctuation control under the condition of ambient temperature 32℃ and target temperature 65℃. The simulation results show that after 2.9 minutes of gradual adjustment, the proposed algorithm converges the steady-state error from the initial 3.8% to 0%, and the overshoot maintains the theoretical zero value. In this study, the backpropagation mechanism is innovatively integrated with incremental PID, and the dynamic correction of the control output is realized through the feedforward network interface module (FB1). The contradiction between overshoot suppression and speed regulation of large inertia system is solved. The research results also fully verify the deep collaborative breakthrough of neural network and classical control in time-delay systems, which has important promotion value for the accurate temperature control of intelligent manufacturing equipment.