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Applications in commercial and military fields created high demands on the steering performance of multi-axle vehicle. With the characteristic of more degrees of freedom (DOF), all-wheel cooperative steering is more conducive to improve the steering performance of multi-axle vehicle. This paper studies multi-axle vehicle assembled with steer-by-wire system, and proposes a control strategy to achieve all-wheel cooperative steering to improve the low-speed steering flexibility and high-speed steering stability of multi-axle vehicle. Based on the ideal steering performance at low-speed and high-speed, the steady-state gain of multi-axle vehicles at different speeds is reshaped. Also, the corresponding vehicle reference model is constructed to provide the ideal vehicle state as a reference. The precision of the vehicle reference model is verified by an all-wheel independent steering platform.

The mathematic model and co-simulation model for distributed-drive articulated heavy vehicles (DAHVs) are developed along with the techniques for its satisfactory verification. The objectives of this paper are to introduce and verify the researches about the assist-steering method for DAHVs. The theory of this proposed assist-steering method in this paper distinguishes it from the traditional direct yaw moment control (DYC) method or assist-steering methods in the previous studies. Furthermore, the co-simulation model developed by MATLAB/Simulink, ADAMS, and AMESim is more reasonable than the traditional methods with simple virtual models, which can replace the real test vehicle for the verification of proposed assist-steering method. Field tests were conducted with a 35t DAHV to verify the models with the comparison of vehicle responses.

The study of vehicle state estimation performance especially on the aspect of observer-based control for improving vehicle ride comfort and road handling is a challenging task for vehicle industry. Since vehicle roll behavior with various road excitations act an important part of driving safety, how to accurately obtain vehicle state under various driving scenes are of great concern. However, previous researches seldom consider coupling relation between vehicle vertical and lateral response with steering input under various road excitation. To address this issue, comprehension analyses on vehicle roll state estimation with coupled input are present in this paper. A full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first created to describe vehicle lateral and vertical coupling dynamics.

Intelligent autonomous articulated vehicles (IAAVs), the most important transportations of intelligent mining system, are the future direction of mining industry. Though it could realize the unmanned drive, without supports of hydraulic steering process analyses and vehicle dynamic researches, there are no references for the IAAVs to adjust the steering angle in certain driving error. It still has to check the signal from the angle sensor repeatedly to track the planned path in the working process, which lead to the low control accuracy. In this paper, the theories of hydraulic steering process and vehicle model will be developed for the vehicle intelligent control with the analyses of road and tire characteristics based on the principle of least resistance.

The traditional hydraulic steering mode in articulated motor-driven vehicle makes the vehicle structure complex. Further more, the forces between the front and rear part of the articulated vehicle could damage the articulated joint body in the process of vehicle steering. However, skid steering mode could make the vehicle steer with the different speed of each wheel, which is flexible without hydraulic steering system. The purpose of this paper is to introduce the principle of skid steering mode in articulated motor-driven vehicle. In this paper, the theory of traditional wheeled vehicle’s skid-steering mode and hydraulic steering mode of articulated vehicle are used to establish the in-situ skid-steering kinematic and dynamic model. Based on the model, the vehicle trajectory and the dynamic relationships among the body structure of the vehicle, longitudinal forces, lateral forces of each wheel are described.

Off-road dump truck body is exposed to abrasive wear during handling of granular materials. The wear rate of body of dump truck has direct influence on maintenance and replacement during its service process. In this paper the discrete element method (DEM) is used to simulate the granular materials of dump truck. The wear of body floor during one dumping process can be achieved by cosimulation of FEM-DEM. The wear depth variation of body has the stochastic characteristic which can be modeled by Geometric Brownian Motion (GBM). The two parameters in the stochastic differential equation, drift coefficient and diffusion coefficient, can be estimated by the wear depth measuring data. It is possible to quantitatively predict the wear evolution of every grid point of the body floor by solving this stochastic differential equation. The simulation result of the wear model is helpful to optimize design of off-road dump truck body.