Recent Advances in Energy-Efficient Fractional-Order PID Control for Industrial PLC-Based Automation: A Review
(1) Sandra Francis Mail (Symbiosis International (Deemed) University, India)
(2) * Pritesh Shah Mail (Symbiosis International (Deemed) University, India)
(3) Abhaya Pal Singh Mail (Norwegian University of Life Sciences (NMBU), Norway)
(4) Ravi Sekhar Mail (Symbiosis International (Deemed) University, India)
*corresponding author

Abstract


Through intelligent control and data-driven decision-making, Industry 4.0 transforms industrial automation by combining the digital, physical, and virtual worlds. The use of advanced control techniques, especially Fractional-Order PID (FOPID) controllers, has drawn a lot of attention due to the rising need for accurate and energy-efficient industrial automation. By examining recent developments in the application of energy-efficient FOPID controllers for Programmable Logic Controller (PLC) based automation systems, this review tries to bridge a gap in the body of literature. The study thoroughly examines more than ten years of research, classifying contributions according to optimization, fractional calculus approximations, and control design techniques. The reported results from various studies are compared using key performance indicators like energy consumption, ISE, ITAE, and IAE. The results show that FOPID controllers continuously perform better than classical PID in terms of energy efficiency, robustness, and control accuracy. However, there are still difficulties in striking a balance between real-time constraints and computational complexity, particularly in industrial settings. This review emphasizes how FOPID controllers can be used to achieve automation that is Industry 4.0 compatible, adaptive, and energy-efficient. It also emphasizes the necessity of future studies into hybrid optimization and lightweight implementation for nextgeneration PLC systems, as well as the need for standardized benchmarking frameworks.


Keywords


PLC-Based Fractional Control; Metaheuristic Optimization in Control; Energy-Efficient Industrial Automation; Adaptive Fractional Controllers; Hardware in Loop Testing

   

DOI

https://doi.org/10.31763/ijrcs.v5i2.1825 		      

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