Abstract
The learning-based model predictive control (LB-MPC) is an effective and critical method to solve the path tracking problem in mobile platforms under uncertain disturbances. It is well known that the machine learning (ML) methods use the historical and real-time measurement data to build data-driven prediction models. The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. The LB-MPC combines the advantages of ML and MPC. In this work, the LB-MPC technique is summarized, and the application of path tracking control in mobile platforms is discussed by considering three aspects, namely, learning and optimizing the prediction model, the controller design, and the controller output under uncertain disturbances. Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed.
Highlights
The path tracking control is a core technique in autonomous driving
We review the application of learning-based model predictive control (LB-model predictive control (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
The prediction model based on machine learning (ML) methods and MPC is the best solution for path tracking in cooperative autonomous unmanned aerial vehicles in the cases of different formation [88,89,90]
Summary
The path tracking control is a core technique in autonomous driving. It is used to control driving in mobile platforms, such as vehicles and robots, along a given reference path, as well as to minimize the lateral and heading errors [1,2,3]. MPCensure has better the control system The can combination intelligently of achieve the learning established goals the performance in path tracking control. The LB-MPC technique rigorously combines statistics and learning with engineering while providing levels of guarantees about safety, robustness, and convercontrol engineering while providing levels of performance guarantees about and gence. It handles system constraints, optimizes withsafety, respectrobustness, to a cost funcconvergence. Section 5three concludes this Section paper regarding the learning-based model predictive technique andSection its application in the field mobile some research challenges faced bycontrol. Garding the learning-based model predictive control technique and its application in the field of mobile platforms for path tracking control
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