## 2 Overview of Learning-Based Model Predictive Control
### Machine Learning  
Recent successes in the field of ML, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. The ML techniques have been widely used to solve complex engineering problems, such as object classification, object detection, and numerical prediction [42; 43]. The ML techniques build statistical models on the basis of the training data. The resulting models are then used to predict and analyze new data on the basis of the data-driven controller [44]. Among ML techniques, neural networks have shown great success in regression problems. Additionally, neural network-based modeling outperforms other data-driven modeling methods due to its ability to implicitly detect complex nonlinearities and the availability of multiple training algorithms. In addition, ensemble learning, a machine learning paradigm that trains multiple models for the same problem, has been gaining increasing attention in recent years. By taking advantage of multiple learning algorithms or various training datasets, ensemble learning provides better predictive performance than a single machine learning model. ML techniques offer sophisticated tools for highly automated inference of prediction models from data. The data-driven methods are effective for solving uncertain problems. During the prediction process, an uncertain model is built on the basis of the uncertain data caused by unexpected disturbances and estimation error. Then, the data-driven methods automatically use the rich information embedded in the uncertain data to make intelligent and data-driven decisions [45]. The data-driven methods are also used in the optimal design of the controller. They play an important role in learning and optimizing for the control precision and real-time response. Iterative learning control (ILC) improves the current iterative control input signal by using the data obtained during the previous iteration [46]. As the number of iterations increases, the tracking error decreases. Therefore, ILC is widely used in ML prediction. ILC is a model-based control method that requires high model accuracy. Therefore, the learning-based prediction models are built on the basis of the data-driven methods and ILC [47]. The controller design only depends on the input and output data of the system and in not affected by the accuracy of the model. This combination improves the prediction performance of the controller. In learning-based prediction methods, the uncertain disturbances in the mobile platforms are usually modeled as a Gaussian process (GP). The GP uses an inherent description function to estimate uncertainty [48]. The GP is a function of system state, input, and other related variables, and GP is updated on the basis of the data collected in the previous experiments. It is notable that the GP is also used to represent the dynamics of traditional systems [49]. On this basis, the GP models can be used to model complex motion modes of moving objects and compute the probability distribution of different motion modes. Afterwards, the path can be divided into different GP components to realize accurate and efficient position prediction [50]. This method does not require large number of parameters in arithmetic operations, and the position information in various motion modes can be obtained by the probability and statistical distribution characteristics of the data itself. The learning-based prediction methods adapt to various complex road conditions and improve the path tracking control performance in the mobile platforms. As the learning-based prediction methods are more adaptable to uncertain disturbances, in the previous work, a deep learning-based approach is proposed to accurately predict the throttle and state on the basis of driving data of experienced operators [51]. The driving data comes from the field test of experimental wheel loader with sensors and GPS.