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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Journal of Engineering and Technological Sciences
Sekhar, Ravi
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Recent Advances in Energy-Efficient Fractional-Order PID Control for Industrial PLC-Based Automation: A Review Francis, Sandra; Shah, Pritesh; Singh, Abhaya Pal; Sekhar, Ravi
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1825

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.
Enhancing Random Forest Model Accuracy using GridSearchCV Optimization for Predicting Multi-Cylinder Engine Performance with Hydrogen-Enriched Natural Gas Blends Sutar, Prasanna S; Sekhar, Ravi; Sonawane, Shailesh B; Bandyopadhyay, Debjyoti; Rairikar, Sandeep D; Thipse, Sukrut S; Ganorkar, Hiranmayee
Journal of Engineering and Technological Sciences Vol. 57 No. 5 (2025): Vol. 57 No. 5 (2025): October
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.5.9

Abstract

Diesel generators (gensets) are essential in India for industries, construction, agriculture, and as backup power for hospitals and data centres. Common fuels include diesel, petrol, natural gas, and, increasingly, solar energy, with hybrid systems gaining popularity for improved efficiency and reduced emissions. Diesel gensets remain reliable and cost-effective, especially in remote areas, but growing environmental concerns are driving adoption of cleaner alternatives like compressed natural gas (CNG), bio-CNG, and dual-fuel systems. HCNG (hydrogen-enriched compressed natural gas) gensets are more efficient and environmentally friendly, though they require greater initial investment. Adding hydrogen enhances combustion and reduces emissions. In this study, various HCNG blends were tested on a multi-cylinder, single-speed gas engine. Experimental evaluation of combustion and performance characteristics is typically time and resource-intensive, so Machine Learning (ML) was applied to streamline the process, thereby minimizing the number of required experiments. The engine performance is assessed using the engine dynamometer, whereas the combustion characteristics are obtained from the High-Speed Data Acquisition (HSDA) system. A Random Forest (RF) regression model was developed to predict performance and combustion characteristics for higher HCNG blends from lower-blend data, with hyperparameter optimization used to improve accuracy and minimize overfitting. Predicted values were validated against experimental results, showing strong correlations. Key parameters like Brake-Specific Fuel Consumption (BSFC), Brake Mean Effective Pressure (BMEP), Exhaust Temperature, Maximum In-Cylinder Combustion Pressure (Pmax), Indicated Mean Effective Pressure (IMEP) and Combustion Duration were predicted, with evaluations showing strong correlations between predicted values and actual results.
Co-Authors Abdul Wahab, Noor Izzri Ahmed, Ahmed H. Ahmed, Omer K. Ahmed, Saadaldeen Rashid Ahmed, Shouket A. Almalaisi, Taha Abdulsalam Awad, Raad Hamza Bandyopadhyay, Debjyoti Bektaş, Enes Çiçek, Adem D., Murugesan Francis, Sandra Ganorkar, Hiranmayee Gökşenli, Nurettin Hamad, Hasan Salman Hashim, Abdulghafor Mohammed Hassan, Mohd Khair Hussain, Abadal-Salam T. Iswanto Iswanto Itankar, Nilisha K., Jagatheesan Majdi, Hasan S. Marhoon, Haydar Abdulameer Mejbel, Basim Ghalib Radhi, Ahmed Dheyaa Rairikar, Sandeep Rairikar, Sandeep D Saleh, Ziyaad H. Shah, Pritesh Shihab, Mustafa Abdmajeed Singh, Abhaya Pal Solke, Nitin Sonawane, Shailesh Sonawane, Shailesh B Sutar, Prasanna S Taha, Taha A. Thipse, Sukrut Thipse, Sukrut S Varma, Chetan Warke, Arundhati Yahia, Elaf Hamzah Yassin, Khalil Farhan Zaynal, Hussein I.