metadata dc speed control

This dataset contains cleaned metadata extracted from journal articles related to DC motor speed control, collected for a bibliometric and scoping review analysis. The original data was gathered from the Scopus database, using a structured search query based on the PCC (Population–Concept–Context) framework: Population (DC motors), Concept (speed control strategies), and Context (control applications). Only peer-reviewed journal articles in English, published between 2019 and 2024, were included.

After retrieval, metadata such as titles, abstracts, author keywords, sources, and citation data were cleaned using OpenRefine to standardize terms (e.g., unifying “PID” and “Proportional-Integral-Derivative”). The final dataset, comprising 417 articles, was analyzed using VOSviewer for keyword co-occurrence and citation mapping, and Biblioplot for enhanced visual interpretation.

The hypothesis driving this research is that distinct control strategies and optimization methods for DC motor speed control form identifiable research clusters, which can be mapped to reveal trends, gaps, and emerging areas. The findings show four main clusters, corresponding to fuzzy logic methods, PID with metaheuristics, BLDC motor control in renewable/EV applications, and hardware-based implementations.

Researchers and practitioners can use this dataset to explore thematic developments, identify influential works and authors, and inform future studies in intelligent motor control systems.