## 5 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Application**} & \multicolumn{1}{c}{**Classical**} & \multicolumn{1}{c}{**High Order**} & \multicolumn{1}{c}{**Fractional**} & \multicolumn{1}{c}{**Terminal**} \\ \hline \multirow{3}{*}{Robotics} & Vehicles & [153; 154; 155; 156; 157; 158] & [169; 170; 171; 172; 173; 174; 175] & \\ \cline{2-5}  & Manipulators & [176; 177; 178; 179; 180; 181; 182] & [53; 183; 184] & [183; 184; 185; 186; 187; 188; 189; 190; 191; 192] \\ \cline{2-5}  & Robots & [193; 194; 195; 196; 197] & [198] & [56; 199] \\ \hline \multirow{3}{*}{Electromechanical systems} & Electric generation & [200] & [201] & [202; 203; 204] \\ \cline{2-5}  & Turbines & [205] & & [206] \\ \cline{2-5}  & Electric loaders & [207] & & \\ \cline{2-5}  & Engines & [208; 209] & [210; 211] & [48; 212; 213] \\ \cline{2-5}  & Filters & [214] & [51] & [215] \\ \cline{2-5}  & Machines & [216; 217; 218; 219; 220] & & [52] \\ \hline \multirow{3}{*}{Generic systems} & Based on a nonlinear model & [46; 221; 222; 223; 224; 225; 226] & & [227; 227; 228] \\ \cline{2-5}  & General theory with examples & [54; 229; 230] & [231] & [232] & [232] \\ \hline \multirow{2}{*}{Energy conversion systems} & Batteries & [233] & & \\ \cline{2-5}  & Converters & [47] & [234] & [235] \\ \hline \multirow{3}{*}{Chemical processes} & Tank systems & & [44; 236] & [237] \\ \cline{2-5}  & Heat exchanges & [238] & & \\ \cline{2-5}  & Seeded batch crystallizer & [49] & & \\ \cline{2-5}  & Reactors & [239] & & \\ \cline{2-5}  & Gas collectors & [240] & & \\ \hline \multirow{3}{*}{Medical applications} & Glycemia regulation systems & & [241] & \\ \cline{2-5}  & Medical images & [242] & & \\ \cline{2-5}  & Other medical applications & [243] & [244] & [245] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Applications and SMC schemes. Figure 11: Applications of DDSMC. analysis. Furthermore, by incorporating machine learning algorithms, the system continuously improves its accuracy and efficiency, learning from both historical and current data patterns [53; 155; 157; 177; 180; 208; 227]. The second is the MPC approach, which uses advanced algorithms and predictive models to foresee how variations in process conditions will influence its behavior and adjust parameters to maintain the desired performance. This approach improves resource utilization by preventing unwanted variations and dynamically adjusting parameters to maximize efficiency. In complex industrial environments, MPC increases productivity and promotes more intelligent management [209; 220; 226; 233; 246]. It must be considered that, usually, MPC assumes some known information about the process [59]. The third is the model-free approach, which eliminates the need for a detailed and complex mathematical model of the system and instead uses a generic model whose parameters are estimated online through continuous analysis of the data measured in the process. This feature simplifies implementation in complex or difficult-to-model systems while allowing for greater flexibility, as it can quickly adapt to unexpected changes in environmental or process conditions [209; 220; 226; 233; 246]. Finally, the iPID controller extends classical PID control by utilizing an ultra-local model. This model is valid in a small time window, and its parameters are updated based on the input and output data of the process. The ultra-local model uses derivatives of the process's input and output signals to capture the system's local behavior, which is helpful in the control of systems with fast and changing dynamics [49; 154; 161; 193]. However, its implementation involves greater complexity compared to the traditional PID control. In general, the four strategies report the use of significant computational resources for the forecasting and online parameter-adjusting processes [10; 59]. Regarding data-driven techniques, according to Table 2, the most commonly used approach is model-free [247], followed by adaptive.