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.

The Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is a modern alternative to the conventional PID controller. This paper presents the design of the ANFIS controller for pH regulation in cooling towers based on the dataset from the PID controller. This paper aims to design the ANFIS controller to achieve a lower RMSE than benchmark models, with improved transient response specifications such as rise time, settling time, and overshoot to optimize pH regulation characteristics. One of the fundamental requirements for designing the ANFIS controller is the availability of a dataset. The challenge in the design process is how to prepare the dataset. This issue was addressed by recording the PID controller dataset, which includes the error (e), the change in error (∆e), and the output. The methodology consists of the following steps: (1) modeling the pH loop of the cooling tower; (2) tuning a PID controller using the Particle Swarm Optimization (PSO) algorithm; (3) recording the PID controller's dataset, including error (e), change of error (∆e), and output; (4) training the ANFIS model using the MATLAB ANFIS Toolbox; and (5) enhancing the transient response by modifying the dataset. Following Instructions for design, the simulated results showed that the ANFIS controller achieved a root mean square error (RMSE) of 0.0081. The transient response characteristics of the best-performing ANFIS controller (Modified ANFIS_4) include: rise time = 0.5863 s, settling time = 1.4867 s, overshoot = 2.7958%, and peak = 7.6548. In comparison, the baseline PSO-tuned PID controller yielded a rise time of 0.6046 seconds, a settling time of 2.3155 seconds, an overshoot of 8.6770%, and a peak value of 8.0916. The results confirm that the ANFIS controller outperforms the PID controller in all key transient response parameters, offering improved accuracy, faster stabilization, and reduced overshoot. These findings demonstrate the effectiveness of the ANFIS design based on real PID controller data, supported by Instructions for design for reliable implementation in nonlinear industrial control systems.