Intelligent PID-based control systems for adaptive and sustainable manufacturing

PID controllers remain one of the widely used control strategies in the industrial systems because of their simple structure, ease of implementation and reliable performance. However, their effectiveness remains limited in non-linear, uncertain and time varying environments. To address these limitations, this study explored the literature on PID and Fuzzy PID controllers from Scopus and Web of Science database. The study reviews the architectures, tuning methods, intelligent integrations, industrial applications and future research directions. The selected studies were examined across multiple dimensions including controller design, rule-base deployment, optimization methods, adaptative and neuro-fuzzy tuning, IoT enabled control, digital twin integration and smart manufacturing applications. The findings of the study show that PID based control has moved beyond conventional tuning methods. Recent studies shows the integration of fuzzy logic, metaheuristic optimization, machine learning, reinforcement learning and digital twins to improve robustness, tracking accuracy and real time monitoring. Fuzzy PID controllers are useful in nonlinear and uncertain operating conditions, while hybrid and AI supported variants offers improved tuning and fault handling capabilities. The review also reveals the application areas of PID in robotics, process industries, additive manufacturing and sustainable industrial operations. Despite of these advances, the literature remains fragmented across domains and lacks in standardized benchmarking and cross sector validation. Based on these research gaps, the study proposes a future research agenda based on self-tuning frameworks, explainable fuzzy rule generation, digital twin supported adaptive control and sustainability oriented multi objective tuning. The present study contributes a comprehensive synthesis of Fuzzy PID research and shows how these controllers are contributing for Industry 4.0.