A vehicle equipped with an articulated steering system is often used in a particular area, such as a mine or the construction field. These sealing or semi-sealing fields make autonomous driving by an ASV (Articulated Steering Vehicle) easier than in passenger cars. Path-tracking is essential for an autonomous vehicle. To improve the path-tracking ability of an ASV, a path-tracking model is established based on dynamic theory. Then, the slide-mode predictive-control (SMPC) strategy is initiated to design the path-tracking controller. The improvement in the path-tracking ability on roads with different adhesion coefficients is validated via the HIL (Hardware in Loop) platform. According to the simulation in HIL with the provided strategy, the path-tracking error during the typical condition decreased by 21.73~93.84%, demonstrating the SMC controller can be used to improve the path-tracking performance of an ASV.

In the context of intelligentization and electrification, the electric power-assisted brake system is an optimal choice for vehicle, allowing precise slip ratio control through oil pressure adjustment to enhance braking effectiveness. Estimating the optimal slip ratio is critical for slip ratio control. Current algorithms for estimating the optimal slip ratio is still room for improving the accuracy and efficiency, especially under the changing road surface, introducing significant lag. This research proposes a self-adaptive optimal slip ratio estimation method for road surfaces and a slip ratio control strategy. Firstly, the Burckhardt tire model is adjusted to better represent the utilizing adhesion coefficient-slip ratio characteristic of the tire actually used. Then a tire model parameter identification algorithm based on road surface similarity and Adam gradient descent is developed. During the identification, the initial value selection method and the segmented mean method are combined to improve accuracy and efficiency. Additionally, decision tree is employed to detect the road surface change, with the sample data elimination and compensation methods, achieving less lag in road surface adaptation for optimal slip ratio estimation. Finally, the deviation between the actual and optimal slip ratio is taken to determine the target oil pressure. Coupled with single neuron PID controller, the slip ratio of vehicle is effectively controlled. Results from the Hardware-in-the-Loop experiments conducted on different road surfaces demonstrate that the proposed optimal slip ratio estimation algorithm achieves superior accuracy and efficiency. Additionally, compared to sliding mode control, the proposed slip ratio control strategy exhibits enhanced robustness.

The current pure electric vacuum vehicle is equipped with main and auxiliary motors, and the two motors work independently without affecting each other. The traditional auxiliary motor usually operates with constant power while the main motor is only responsible for the vehicle driving. The lack of cooperation between the two motors results in high energy consumption. Therefore, formulating a reasonable strategy for the two motors has a significant effect on the performance of the vacuum vehicle. This paper takes a pure electric vacuum vehicle as an example to propose an energy management strategy based on fuzzy control. First, for the working motor, a fuzzy controller is designed by taking the vehicle speed and acceleration as input and motor speed and torque as output. Therefore, the vacuum vehicle can automatically adjust the operating power of the cleaning system according to the real-time road conditions; the driving motor control strategy adopts a closed-loop control strategy that combines driver input and vehicle state parameter feedback based on considering the operating motor. Finally, the effectiveness of the strategy is verified by simulation. The results show that the energy-saving control strategy effectively reduces the power consumption per 100 km and increases the driving range, which is of great significance to the development and design of the vacuum vehicle.