Adaptive and Self-Tuning PID Control Strategies for Improving Stability and Efficiency in Next-Generation Smart Manufacturing Environments

Abstract

Smart manufacturing environments of the coming generation are very highly automated, connected cyber-physical systems, dynamic production demands and real-time exchange of data. The most commonly used industrial control technique has been conventional Proportional-Integral-Derivative (PID) controllers because of their simplicity and reliability, but fixed-parameter PID controllers are often unable to achieve optimum behavior when faced with nonlinearities, time-varying disturbances, and uncertainty within the system, as is common in smart factories. The present paper suggests an adaptive and self-tuning PID control system that was developed to improve stability, responsiveness, & energy efficiency in Industry 4.0 production facilities. The algorithm combines web-based parameter control via recursive estimation and tuning with optimization-based parameter control via real-time sensor responses. The use of simulation and experimental validation on a representative manufacturing process shows that there are better responses in a transient situation, lesser steady state error, and better disturbance elimination, in addition to quantifiable energy savings over a classical PID set-up.The suggested solution has its benefits, but it comes with computational costs, the complexity of its implementation, and the requirement of good-quality sensor data. The practical limitations are related to the integration with the legacy industrial controllers and the cybersecurity threats in the interconnected systems. Future research plans involve implementing machine-learning-assisted predictive tuning and a distributed control system to be used in large-scale smart factories and creating standardized industrial frameworks to be used in deploying adaptive PID systems. The results confirm that adaptive and self-tuning PID strategies may be considered an effective direction of resilient, efficient, and intelligent control in the next-generation manufacturing systems.