Articulated Vehicle Research Articles

Integrated Path Tracking Controller of Underground Articulated Vehicle Based on Nonlinear Model Predictive Control

This paper proposes an integrated path tracking controller for articulated vehicles. A nonlinear model-predictive control (NMPC)-based reference state tracker is designed as an upper-level controller to solve the vehicle’s longitudinal velocity and steering rate. A terminal cost is introduced into the NMPC to improve the controller’s stability. A lower-level controller is developed to translate upper-level solutions into vehicle actuators’ signals, including steering and driving controllers. The steering controller translates the steering rate into the linear velocity of the cylinder to calculate the required fluid volume and ultimately into the rotation speed of the steering motor. The neural network method is applied in the driving controller to ensure accuracy under different loadings. In order to investigate the effects of the path tracking controller, an articulated dump truck is adapted for the field tests by adding the steering-by-wire system and driving-by-wire system, respectively. Experimental verifications of the lower-level controller are performed. The results show that the controller can accurately satisfy the demand. Finally, the tracking performance of the integrated path tracking controller is analyzed experimentally under different reference velocities. The results indicate that tracking accuracy can be guaranteed.

Adaptive Articulation Angle Preview-Based Path-Following Algorithm for Tractor-Semitrailer Using Optimal Control.

Most existing Path-Following Algorithms (PFAs) are developed for single-unit vehicles (SUVs) and rarely for articulated vehicles (AVs). Since these PFAs ignore the motion of the trailer, they may cause large tracking deviations and ride stability issues when cornering. To this end, an Adaptive Articulation Angle Preview-based Path-Following Algorithm (AAAP-PFA) is proposed for AVs. Different from previous PFAs, in this model, a simple linear vehicle dynamics model is used as the prediction model, and an offset distance calculated by an articulation angle is used as part of the preview distance. An adaptive posture control strategy is designed to trade off the trajectory tracking performance and lateral stability performance during the path-following process. Considering a large prediction mismatch caused by using a linear vehicle dynamics model, a feedback correction method is proposed to improve the robustness of the steering control. In the comparison simulation experiment with SUV-PFA, it is confirmed that the novel PFA has better adaptability to the contradictory relationship between tracking performance and lateral stability and has strong steering control robustness.

A new automated motion planning system of heavy accelerating articulated vehicle in a real road traffic scenario

The main purpose of this study is to develop a novel motion planning for an articulated vehicle (AV) in real traffic situations. This motion planning generates collision-free and feasible trajectories based on kinematic and dynamic analyses of the AV concerning its surrounding vehicles. For this purpose, the collision-free trajectories are simulated in the presence of other vehicles, when the AV is conducting a lane change manoeuvre. A new method is utilised to derive the feasible trajectories by taking into account 3-D surface of the slip angle, roll angle, and lateral acceleration of the AV. This paper presents a new approach to generate the trajectory of an accelerating AV considering the surrounding vehicles in manoeuvre, which are either accelerating or decelerating. The optimal trajectory is then obtained based on the longitudinal acceleration of the AV and the time duration of the lane change manoeuvre, aimed at trajectory tracking control. Therefore, a 3-DOF dynamic model of the AV, including the yaw-rate, lateral velocity of the tractor and articulation angle, is developed. The tyres dynamic is simulated using non-linear Dug-off model. Furthermore, an innovative trajectory tracking control system is proposed concerning a sliding mode control. The developed dynamic model of the AV is verified by the Truck-Sim model. Results show that the collision-free and feasible trajectories can be generated based on the newly presented method of trajectory planning. The outcomes of the trajectory tracking control as the final part of the motion planning system indicate that the heavy articulated vehicle can be guided according to the new automated motion planning.

Real-Time Video Display System in Articulated Vehicle for Road Safety Enhancement

Real-Time Video Display System in Articulated Vehicle for Road Safety Enhancement

Path planning and tracking for autonomous mining articulated vehicles

This paper presents a path planning and tracking framework for autonomous mining articulated vehicles (AVs). The relation space method is proposed for path planning. In this method, a self-organising, competitive neural network is adopted to identify the space around the vehicle. Then, the vehicle's optimal driving direction is determined by using the spatial geometric relationships of the identified space. A proportional-integral-derivative (PID) controller is used to control the vehicle speed, whereas a model predictive control (MPC) is designed for steering control, where the path tracking error model is used for MPC development. In addition, the AV's dynamics model is built in Adams. The effect and sensitivity of the path-tracking controller are validated through the Matlab-Adams co-simulation. The test results show the satisfactory path-tracking performance.