## 6 Conclusions  
Despite the numerous advances in data acquisition, processing, analysis systems, and intelligent algorithms, the design of sliding mode control (SMC) based on data-driven techniques continues to captivate the academic community's interest. This subject shows no signs of slowing down in scientific production, with projections indicating that research output will continue to climb, possibly reaching its zenith by 2048.  
Although the classical approach to SMC is still widely used because of its simplicity in implementation and the availability of mathematical tools for analysis, there has been a noticeable increase in research works that focus on terminal sliding mode control. This trend shows the advantages offered by this scheme, particularly in terms of robustness and convergence properties.  
Articles that focus on the treatment of generic systems are very useful for addressing specific applications through tailored models and methodologies. They establish foundations of system dynamics and control principles that can be adapted and applied to a wide range of practical scenarios. This approach facilitates the translation of theoretical advancements into practical solutions in the field of SMC.  
Robotics is the primary field where data-driven techniques have been used recently in order to enhance control performance and autonomy. This leads to opportunities to apply these techniques in fields such as industrial chemical processes and medical applications, where precise and robust control is paramount.  
The exploration of alternatives that facilitate the implementation of TSMC and FOSMC controllers is important given the existing constraints in integrating them into industrial/commercial controllers like programmable logic controllers (PLCs), microcontrollers, single-board computers, and others. The constraints associated with compatibility and scalability need to be addressed to facilitate the widespread adoption of these advanced control techniques.  
Conceptualization, D.C.-C., O.E.C. and R.E.V.; methodology, D.C.-C., N.L.P. and L.M.S.-C.; validation, O.E.C., F.C. and R.E.V.; formal analysis, D.C.-C., N.L.P., A.O.-D., L.M.S.-C., F.C., O.E.C. and R.E.V.; investigation, D.C.-C., N.L.P., A.O.-D., L.M.S.-C., F.C., O.E.C. and R.E.V.; writing--original draft preparation, D.C.-C., N.L.P. and O.E.C.; writing--review and editing, D.C.C. and R.E.V; supervision, A.O.-D., F.C., O.E.C. and R.E.V. All authors have read and agreed to the published version of the manuscript.  
This work was developed with the funding of Universidad de Medellin UdeM, Universidad Pontificia Boliviana UPB, Universidad Eafit, and Universidad San Francisco de Quito USFQ.  
Not applicable.  
Data Availability Statement: Data are available upon request from the authors.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANFIS & Neuro-Fuzzy Inference System \\ CRONE & Controle Robuste d'Ordre Non-Entier \\ DDSMC & Data-Driven Sliding Mode Control \\ FOSMC & Fractional-Order Sliding Mode Control \\ FTSMC & Fast Terminal Sliding Mode Control \\ HIL & Hardware In the Loop \\ HOSM & Higher-Order Sliding Mode \\ HOSMC & Higher-Order Sliding Mode Control \\ HOTSMC & Higher-Order Terminal Sliding Mode Control \\ iPID & Intelligent Proportional-Integral-Derivative \\ IAE & Integral Absolute Error \\ ISE & Integral Square Error \\ ISMC & Integral Sliding Mode Control \\ ITAE & Integral Time Absolute Error \\ ITSE & Integral Time Square Error \\ ITSMC & Integral Terminal Sliding Mode Control \\ MAE & Mean Absolute Error \\ MBC & Model-Based Control \\ MFC & Model-Free Control \\ MPC & Model Predictive Control \\ MIMO & Multi-Input-Multi-Output \\ NTSMC & Nonsingular Terminal Sliding Mode Control \\ PID & Proportional-Integral-Derivative \\ PLC & Programmable Logic Controller \\ RMSE & Root Mean Square Error \\ \end{tabular}