## 2 Overview of Learning-Based Model Predictive Control
### Learning-Based on Model Predictive Control  
The MPC method provides an integrated solution for the control system with interactive variables, complex dynamics, and constraints, but it relies on the accuracy of the physical model [52]. In order for the control performance to be improved, usually more complex models are built and non-linear optimization techniques are used. The ML methods make predictions by the data-driven methods [53; 54]. The ML methods build  
Figure 3: The flowchart of learning-based prediction method to accurately predict throttle and status for wheel loaders [51].  
statistical models on the basis of training data without model identification and use historical data to derive control strategy [55; 56; 57]. The ILC is applied to improve the predictive performance of the controller [58]. Theoretically, with the accumulation of valid data, the prediction ability of ML continuously improves. However, the prediction performance of ML significantly depends on the amount of training data. The perfect combination of the MPC and ML forms the LB-MPC. Applying the learning algorithm to the MPC will improve the performance of the system and guarantee safety, robustness, and convergence in the presence of states and control inputs constraints.  
Most research has focused on an automatic data-based adaptation of the prediction model or uncertainty description. The feedback techniques have the ability to overcome and reduce the impact of uncertainty [59]. The LB-MPC embeds the ML method in the MPC framework to eradicate the influence of uncertain disturbances, thus improving the performance of path tracking in mobile platforms [26; 60]. The LB-MPC decouples the robustness and performance requirements by employing an additional learned model and introducing it into the MPC framework along with the nominal model. The nominal model helps to ensure the closed-loop system's safety and stability, and the learned model aims to improve the tracking behaviors. The LB-MPC effectively evaluates both the current and historical effects of uncertainties, leading to superior estimating performance compared with conventional methods.  
The combination of MPC and ML in nonlinear control systems has been a focus of industrial control research and development [61]. First, the LB-MPC method is used to train the model on the basis of the input data and ML, such as the GP regression method [62; 63; 64; 65]. Second, the MPC control strategy is generated, and the calibration of MPC control parameters is performed by directly learning from the data on the basis of ML. In order for the real-time response of the controller to be realized, the sample database is trained offline by using the deep neural network [66; 67; 68; 69]. Afterwards, non-direct measurement and the state variables for MPC are designed on the basis of ML, such as reinforcement learning [70]. Finally, the controller output is optimized on the basis of the security control framework.  
In this work, we review the application of LB-MPC for path tracking control in mobile platforms, including learning and optimizing the prediction model, the controller, and the controller output in the presence of uncertain disturbances.