## 4 Future Research Challenges  
For the interpretability of the prediction model, the prediction model built using the ML method is a kind of black box model and is applicable to specific small-scale environment. However, once the learning fails, it is difficult to ensure the system security based on ML only. Most of the traditional physical models are simplified, linearized, and theoretical. However, the complex modeling process is time-consuming, and the model has calculation errors. The model built by using the LB-MPC belongs to the gray-box model. When the advantages and characteristics of ML and MPC are combined, the LB-MPC method achieves interpretable optimal performance. However, it has poor adaptability to the path without learning tests.  
For the accuracy of the prediction model, the traditional prediction model simplified by expressions is not comprehensive because it does not consider the influence of uncertain disturbances. The data-driven prediction model is constructed on the basis of the data generated by the actual operations in mobile platforms. It has good adaptability and higher accuracy in the special working conditions. However, the data-driven prediction model requires a large number of labeled data, and the reinforcement learning model needs to interact with the environment. In order for the unexpected states to be dealt with, both prediction models require a large amount of training time. Reducing the volume of data required by the data-driven prediction model and promoting greater adaptive capacity are challenges in the future.  
For controller design, only small random uncertainties can be handled in the mobile platform path tracking controller design, and all behavioral responses under uncertain disturbances cannot be considered. It is necessary to continuously learn from the uncertain disturbances during the operation process in order to improve the performance of the controller.  
For the real-time capability of LB-MPC, the data may not capture the mobile platform operation characteristics when it is controlled by MPC. The mobile platform model also requires continuous updating to capture the changes of some physical characteristics over time due to changes in external and internal factors, such as weather, complex curvature variation conditions, mechanical friction, and fatigue failure. However, the automatic model update mechanism is an important challenge for the LB-MPC systems proposed in previous studies. The way to solve the challenge in the future is to develop a self-adaptive ML-based prediction model that exploits online mobile platform operation data to update the prediction model continuously as the mobile platforms are controlled by MPC in real time.  
For the optimization of the controller, the input and output constraints limit the path tracking the performance in mobile platforms. The current security control framework combines the model predictive security filters with different types of learning-based control methods. It solves the trade-off problem between high performance and security assurance faced by the control of complex systems. In order for the performance of path tracking controllers to be further improved under uncertain disturbances, the seeking of more reasonable optimization methods and addressing input and output constraint methods have great research potential.