A PLC Parameter Self-Tuning Method Based on Particle Swarm Optimization (PSO) Algorithm

PLC control holds a pivotal position in industrial production, as its functionality has a direct bearing on the operational efficiency and the quality of products across the entire production line. The efficiency metrics of PLC, including its response time and stability, hinge heavily on the choice of its parameters. Therefore, the automation of optimizing and fine-tuning PLC parameters holds immense significance in enhancing the performance of control systems. The Particle Swarm Optimization (PSO) algorithm, a cutting-edge intelligent optimization technique, excels in its robust global search capabilities and swift convergence rates when tackling intricate optimization challenges. Leveraging this algorithm for PLC controller parameter tuning can lead to the automatic discovery of the optimal parameter combination, thereby optimizing the PLC control system's performance. This article introduces a PLC parameter self-tuning approach grounded in the PSO algorithm. This innovative method mimics the natural collaborative behavior of particle swarms and explores the parameter space to identify the optimal solution. Experimental findings reveal that this PSO-based PLC parameter self-tuning approach significantly enhances the control system's performance. This method not only identifies the optimal parameter combination automatically but also demonstrates superior optimization efficiency and stability.