## 5 Results
(2018) used deep reinforcement learning to self-tune the FOSMC parameters, using a data-driven dynamic model of the static VAR compensator in a wind generation system. The use of reinforcement learning enhanced the robustness against disturbances thanks to the FOSMC's self-tuning abilities. It should be noted that reinforcement learning adapts to environments with parameter variations models caused by external factors ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data-Driven** & **Classical** & **High Order** & **Fractional Order** & **Terminal** \\ \hline Adaptive & & [46,49,153,155–160,162, 165,171,176,177,179–181,194,195,205,208,214, 217–219,221–224,230,238–240,242,248] & [200,234,241] & [51,53,183,201,2  
Additionally, the performance indexes were analyzed, finding that the most used are based on error measures, such as RMSE (root mean square error), MAE (mean absolute error), MSE (mean square error), ITAE (integral time absolute error), IAE (integral absolute error), ISE (integral square error), ITSE (integral time square error), and steady-state error; settling time and overshoot have also been used. Few articles review the convergence time and control effort or define customized error functions. Furthermore, the articles were classified according to their tests, determining whether they were simulated or implemented through a real or scaled prototype. The analysis revealed that 56% of the articles employed simulation software. In contrast, 18% used prototypes, while 26% incorporated a combination of simulation and prototype testing. The following analysis considered only the total number of articles that realized implementation. The classical SMC was implemented in 30.9% of cases, followed by ITSMC and NTSMC each at 16.4%, FOSMC at 14.5%, and both HOSMC and FTSMC at 9.1% each. TSMC and HOTSMC had the lowest implementation rates, each comprising 1.8% of the cases. The most implemented data-driven techniques were the model-free approach at 31.6% and the adaptive one at 23.7%, followed by the iPID controller at 5.9% and the predictive approach at 1.5%. On the other hand, fuzzy and neural network algorithms were the most implemented with an equal number, 38.9% each. By contrast, reinforcement learning was the least implemented, 22.2%. In particular, neither fuzzy logic nor reinforcement learning algorithms have been reported to be implemented in chemical processes. Regarding the reported prototypes, the hardware in the loop (HIL) technique was utilized in various studies to test embedded control systems in applications such as converters in energy systems, robotic manipulators, spacecraft, and motors applications [47; 21; 234; 248; 250]. The dSPACE hardware platform was combined with Matlab(r) to streamline the design, testing, and simulation of real-time systems, specifically in dSPACE real-time control boards D51103, D51104, D51105, and D51202. Other prototypes were implemented using NI DAQ systems connected, in most cases to Matlab/Simulink(r) and a smaller number to LabVIEW(r)  
Chattering is the most significant issue in implementing SMC, which has led many studies to propose methods for mitigating this effect. Approximately 60% of the documents reviewed reported incorporating strategies to address chattering. Of these, about 24% employed the boundary layer method, which involves replacing the discontinuous sign function with smooth, continuous functions such as the sigmoid, hyperbolic tangent, or saturation. As expected, the primary SMC scheme utilizing this strategy was classical, followed by fractional-order and nonsingular. It is also noteworthy that the literature reports that strategies like HOSMC and FOSMC have been shown to reduce chattering effectively, and this was observed in articles such as [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] et al. (2018); [PERSON] et al. (2011); [PERSON] and [PERSON] (2013); [PERSON] et al. (2019), among others.