## 4 Bibliometric Analysis
### Analysis of Publications  
The Bibliometrix package in R simplifies conducting a comprehensive analysis of the document dataset, enabling the identification of key concepts and terms that frequently appear in elements such as the title, abstract, and body of each article [145; 148]. This analysis reveals research trends and provides insight into the evolving dynamics of the topic of interest over time [145; 146].  
The relationships between terms are visualized in the co-occurrence network in Figure 7. There are five main nodes, each uniquely identified by a different color. The purple node represents core concepts of sliding mode control, integrating terms like robustness, neural networks, and finite-time convergence. The green node highlights data-driven control techniques, including model-free adaptive control and prescribed performance. Meanwhile, the blue node focuses on model-free control approaches, linked to tracking control and sliding mode observers. The red node groups topics related to time-delay estimation and adaptive control strategies.  
The thematic evolution graph facilitates the construction of a historical context by delineating keyword relationships over a specific time frame. This information identifies emerging research topics and discerning temporal trends [145]. The analysis was segmented into four periods, a shown in Figure 8. The initial period involved the design of the control system based on the mathematical model. In the second period, the focus shifted to model  
Figure 6: Cumulative production model.  
Figure 7: Co-occurrence network of keyword plus. Performed with VOSviewer 1.6.20.  
free control techniques, alongside a focus on SMC and robust control, mainly applied to vehicles. The third period spotlighted SMC and tracking control, with additional attention on topics such as adaptive control, the design of sliding mode observers, and higher-order SMC. The most recent period, the fourth, showcased diversified research interests, emphasizing topics such as terminal sliding mode, optimization methods, discrete-time controller design, and the development of controllers for disturbance rejection and fail detection systems.  
The thematic map is a coordinate system of two axes based on co-words and keyword cluster analysis. The abscissa axis is the centrality or relevance grade and is a measure of the intensity of connections among clusters. On the other hand, the ordinate axis is the degree of density or development and characterizes the strength of the links that connect words within the cluster [151; 152].  
Figure 9 shows the thematic map of the dataset. In motor themes (quadrant 1), which are highly relevant and well developed, model-free SMC takes precedence, followed by data-driven control and learning systems. The design and implementation of data-driven control techniques involve discrete-time system analysis. Within the same quadrant is a group focusing on applications of electrical systems, including motors.  
Figure 8: Thematic evolution of keyword plus.  
Figure 9: Thematic map of keyword plus.  
Quadrant 2 (basic themes) encompasses pertinent topics that have yet to reach a high level of development. The first group includes analysis of nonlinear systems, stability, and trajectory control. Closely following is another cluster where the focus shifts to optimization and neural networks, emphasizing system performance metrics.  
Quadrant 3 (niche themes) is dedicated to specific applications characterized by substantial technological development, including photovoltaic systems, power converters, and power systems. Meanwhile, quadrant 4, where emerging or declining themes are found, encompasses subjects such as fault detection systems, analysis of delayed systems, and power point tracking systems.