The design of SMC can be addressed using the model-based or model-free approach. Although model-based control (MBC) has been the predominant framework, it has limitations, such as dependence on a plant model, which, in many cases, becomes a time-consuming task, especially when dealing with complex or nonlinear systems [18; 19]. Furthermore, the system modeling process is always subject to a modeling error that directly affects the performance of MBC [18; 19; 20]. On the other hand, MBC can become inefficient when dealing with disturbances and scenarios not considered during the design stage [19; 21]. The model-based SMC design is illustrated in Figure 1.  
In contrast, the model-free control (MFC) approach, which operates without explicit system models, has emerged as a viable alternative to mitigate the inherent limitations of MBC. The above is mainly due to its adaptability in formulating control laws and its ability to leverage the advancements of Industry 4.0, which has substantially increased the number of interconnected instruments that provide a large amount of information in real time [18; 19; 22; 23; 24; 25]. Nonetheless, this approach presents certain drawbacks concerning stability, convergence analysis, and computational overhead during controller implementation [19; 26]. Despite these challenges, integrating MFC and MBC frameworks can enhance the overall performance of the control system, using the strengths of both methodologies and offering a promising option to address the shortcomings while taking advantage of the benefits of each technique [18; 19]. The data-driven framework encompasses process modeling, controller design, and performance evaluation tasks, assuming high data availability [19; 27; 28]. Two of the most prominent data-driven control strategies are model predictive and adaptive control [21; 29; 30; 31; 32]. Model predictive control (MPC) was devised to employ dynamic models to forecast the future behavior of the process and optimize the variable manipulated under process constraints, distinct from predictive control based on raw data for prediction, avoiding the need for an explicit model of process [32; 33]. Otherwise, adaptive control embraces a set of techniques that can learn and dynamically adjust the control strategy based on the measured plant data [29; 30]. Some versions of model-based adaptive control rely on a plant or a disturbance model, while data-driven adaptive control only requires the measurements obtained from the system [21; 29]. In addition, there are other data-driven control techniques, such as model-free schemes. The iPID (intelligent PID) controller is a designation for model-free controllers that incorporate elements of the traditional PID controller, including iterative feedback tuning [21; 34; 35; 36], and hybrid schemes, such as the proposal by [PERSON] et al. [37], which combined an active disturbance rejection controller SMC with nonlinear gains that are a function of the tracking error and an adaptive extended state observer for temperature regulation in an electric furnace. Nevertheless, this article  
Figure 1: Model-based SMC design. emphasizes adaptive, predictive, iPID, and model-free schemes because they are frequently found in reviewed articles. MFC exploits the measured input-output data of the process for the controller designing, and it does not concern the dynamics process since it treats it as a black box [19; 21; 28; 29; 38]. MFC can be designed through a training process employing machine learning techniques, learning from plant behavior, and using approximate dynamic programming or reinforcement learning. Consequently, intelligent algorithms, including neural networks, optimization algorithms, and fuzzy systems, are strongly connected to data-driven techniques [19; 28; 29]. Regarding SMC based on a model-free approach, there are several proposals for different sliding mode schemes, each offering unique insights and methodologies [39; 40; 41; 42; 43; 44; 45].