## 5 Results
Some proposals employ the adaptive scheme to adjust the controller, estimator, or plant model parameters. The iPID controller appears only in robotics, chemical processes, and electromechanical systems applications. In addition, MPC has the lowest number of associated items and has been used for robotics, chemical processes, and medical applications. The adaptive and model-free approaches have been used combined in all applications. Currently, iPID controllers, mixed with adaptive and model-free schemes, have also been employed in robotics, chemical processes, and electromechanical systems. Additionally, the predictive scheme has been integrated with the model-free approach in robotics and other applications. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Adaptive** & **Predictive** & **Model-Free** & **iPID** \\ \hline \multirow{5}{*}{Robotics} & [53; 99; 10; 153; 155–160; 162; 165; 167; 167; 171; 173; 175; 177; 179–181; 181; 183–187; 194; 195; 248] & [55] & [55; 56; 99; 101; 153; 157–167; 167; 168; 169; 170; 172; 174; 175; 177,180; 182,186; 181,183–187; 189,192, 194; 195,199,248] & [55] & [154; 161; 182; 193] \\  & 181,183–187,189,192, & & 187,191–191; 197,199,248,249] & [197; 199; 248; 249] \\ \hline Chemical processes & [49; 238–240,250] & [237] & [44; 236; 238–240,250] & [44; 49; 236] \\ \hline Energy conversion systems & [200–206; 213; 234] & & [47; 203; 204; 213; 233, 251] & \\  & & & 234,251] & \\ \hline Electromechanical systems & [51; 52; 57; 208; 214; 215, 217–219,246] & & [48; 51; 52; 57; 58; 207–212; 215–220,235,246] & [211] \\ \hline Generic systems & [46; 221–224; 227; 228; 230] & [54] & [46; 54; 221; 222; 224–227; 229; 231; 232] \\ \hline Medical applications & [241; 242] & [241; 242; 244] & [245] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Applications and data-driven techniques. The second part of the analysis focused on SMC schemes and data-driven techniques, as illustrated in Table 3. The classical SMC remains predominant and has been successfully integrated into all data-driven techniques. Also, terminal- and fractional-order schemes have been combined with adaptive and model-free approaches. Further, the iPID controller has been utilized with high-order and fractional-order schemes. Again, the model-free approach has more publications, followed by adaptive, iPID, and predictive approaches. As indicated in Table 4, the SMC schemes and intelligent algorithms were also analyzed. It is worth highlighting that only some articles explicitly report the intelligent algorithm used; because of this, Table 4 reports fewer documents. In this case, neural networks are the most algorithm employed, followed by fuzzy and reinforcement learning algorithms. The adaptive, recurrent fuzzy, radial basis function, and adaptive radial basis function neural networks are some reported types. Among these, radial basis function neural networks emerge as the most frequently cited instances. Within the intelligent algorithms, reinforcement learning stands out for its potential in implementing model-free control schemes. One of the most interesting works that used this technique is the one proposed by [PERSON] et al. (2018), which successfully employed it in parallel robots. In this case, reinforcement learning was employed to select the dynamical model of the process with variable parameters, improving its performance and robustness, reducing chattering, and maintaining stability in the presence of external disturbances. On the other hand, [PERSON] et al. (2019) utilized SMC to maintain an underwater vehicle's normal operating state and incorporated an auxiliary controller that combined reinforcement learning with a dynamic programming algorithm. It is noted that the proposed strategy learns from the control actions and, in addition, includes a prioritized experience replay (PER(\(\lambda\))) that allows it to prioritize some experiences during the training process. Additionally, [PERSON] et al.