No abstract

With the rapid development of the world economic construction and the shortage of energy, it has become a hot research issue to realize the electrification of the vehicle driving system and improve energy efficiency. Most of the electric construction machinery power systems are characterized by low speed and high load. The coordinated driving of multiple motors can increase the output torque and improve the transmission efficiency of the machine on the basis of a compact layout. A novel configuration of electric construction vehicles based on multi-motor and single-speed and its driving torque distribution control method is presented in this paper. The detailed mathematical model is established and the simulation analysis is carried out based on it. The results show that the proposed multi-motor driving system with the control strategy can improve the overall efficiency in the condition of ensuring the driving force when the parameter matching and motors choosing reasonably.

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.