Dynamic Tuning of PLC-Based Built-In PID Controller Using PSO-MANFIS Hybrid Algorithm via OPC Server

In modern industrial automation, optimizing the performance of Programmable Logic Controller (PLC)-based PID controllers is critical for ensuring precise process control. This study presents a novel methodology for the dynamic tuning of built-in Proportional-Integral-Derivative (PID) controllers in PLCs using a hybrid algorithm that combines Particle Swarm Optimization (PSO) and Multiple-Adaptive Neuro-Fuzzy Inference System (MANFIS). Classical PID tuning methods, such as Ziegler–Nichols and Cohen–Coon, have traditionally been employed in industrial control systems. However, these methods often struggle to address the complexities of nonlinear, time-varying, or highly dynamic processes, resulting in suboptimal performance and limited adaptability. To overcome these challenges, the proposed PSO-MANFIS hybrid algorithm leverages the global search capabilities of PSO and the adaptive learning abilities of MANFIS to optimize PID parameters in real-time dynamically. Integrating MATLAB (R2021a) with industrial automation systems via an OPC (OLE for Process Control) server utilizes advanced optimization algorithms within MATLAB to obtain the best possible parameters for the industrial PID controller, enhancing control precision and optimizing production efficiency. This MATLAB-PLC interface facilitates seamless communication, enabling real-time monitoring, data analysis, and the implementation of sophisticated computational tools in industrial environments. Experimental results demonstrate superior performance, with reductions in rise time from 93.01 s to 70.98 s, settling time from 165.28 s to 128.84 s, and overshoot eliminated from 0.0012% to 0% of the controller response compared to conventional tuning. Furthermore, the proposed approach achieves a reduction in Root Mean Square Error (RMSE) by approximately 56% to 74% when compared with the baseline performance. By integrating MATLAB’s computational capabilities with PLC-based industrial automation, this study provides a practical and innovative solution for modern industries, offering enhanced adaptability, precision, and reliability in dynamic control applications, ultimately leading to optimized production outcomes.