Fractional order control: A bibliometric analysis (2000–2022)

• Published in: Results in control and optimization Vol. 14; p. 100366
• Main Authors: Shah, Pritesh, Sekhar, Ravi, Sharma, Deepak, Penubadi, Harshavardhan Reddy
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-03-2024; Elsevier
• Subjects: FOPID; Fractional calculus; Fractional order controller; FOPID; Fractional order controller; Fractional calculus
• ISSN: 2666-7207; 2666-7207
• DOI: 10.1016/j.rico.2023.100366

Fractional order control is one of the most important enablers of advanced closed loop industrial automation systems. This paper presents a detailed bibliometric study of fractional order control related research published during the past two decades (2000–2022) in the Web of Science (WoS) and Scopus indexed sources. The WoS analysis included yearly publications, publication types and top contributing publishing sources, organizations, researchers, countries and research areas. VoS viewer based full counting methodology was used to analyze citation, co-citation and co-authorship based collaborations among authors, organizations, countries, sources and documents. Co-occurrence, timeline and burst detection analyses of keywords and published articles were also carried out to unravel significant fractional control research trends in the last two decades. The Scopus analysis firstly included annual publication counts, subject areas, document types and top contributing authors, sources, nations, institutions and funding agencies. Secondly, dedicated Scopus searches were carried out for hardware implementation, non integer control and patents. Results indicate that the fractional order controller research has been primarily focused in engineering domain, followed by mathematics, physics, automation control systems and computer science. Materials, mechanics and telecommunications are some of the lesser explored areas. There is a lot of scope of fractional order control research related to patents, hardware implementation, non integer control, anomalous diffusion, chaos and task analysis. Future investigations will also include neural networks based machine learning in fractional order control solutions.