This paper presents a novel model-based method for estimating the attitude of underground articulated vehicles (UAV). We selected the Load–Haul–Dump (LHD) vehicle as our application object, as it is a typical UAV. First, we established the involved models of the LHD vehicle, including a kinematic model, the linear and angular constraints of a center articulation model, and a dynamic four degrees-of-freedom (DOF) yaw model. Second, we designed a Kalman filter (KF) to integrate the kinematic and constraint models with the data from an inertial measurement unit (IMU), overcoming gyroscope drift and disturbances in external acceleration. In addition, we designed another KF to estimate the yaw based on the dynamic yaw model. The accuracy of the estimations was further enhanced by data fusion. Then, the proposed method was validated by a simulation and a field test under different dynamic conditions. The errors in the estimation of roll, pitch, and yaw were 3.8%, 2.4%, and 4.2%, respectively, in the field test. The estimated longitudinal acceleration was used to obtain the velocity of the LHD vehicle; the error was found to be 1.2%. A comparison of these results to those of other methods showed that the proposed method has high precision. The proposed model-based method will greatly benefit the location, navigation, and control of UAVs without any artificial infrastructure in a global positioning system (GPS)-free environment.
Most of the existing research related to the handling stability of articulated vehicles takes the front wheel angle as input and neglects the influence of the steering system and regards these vehicles as moving in planes that parallel the ground plane. The established dynamic model cannot meet the requirements of stability control under unmanned and high-speed working conditions. This research proposed a fusion modeling method based on the kinematics and dynamics of vehicles to improve the precision of a driving dynamic model. The proposed method initially took the steering wheel angle as input to establish the kinematic model for the corresponding relation of the hydraulic system flow and pressure, the articulated angle of front and rear frames, and the output force of left and right steering hydraulic cylinders. A dynamic model considering the axle load transfer and interactions between the front frame and the rear body in a threedimensional coordinate system was then established. A driving dynamic model of an entire vehicle was also constructed by coupling the kinematic and dynamic models. Finally, real vehicle test data of a 60-ton articulated vehicle were used to verify the proposed Automatic Dynamic Analysis of Mechanical Systems (ADAMS) driving model in this study. Results confirm that the error about the steady-state yaw rate in the simulation and real vehicle tests did not exceed 0.01 rad/s. Therefore, the established driving dynamic model can describe the dynamic response accurately. This study concludes that the proposed fusion modeling method based on the combination of kinematics and dynamics can effectively improve the accuracy of the articulated vehicle model and further provide a technical reference for the study on a driving stability control strategy.
Owing to the harsh environment of underground mines, autonomous underground articulated vehicles (UAVs) with precise control and positioning system are particularly important. However, the ambiguity of steering characteristics hinders the development of UAVs. This study presents a model-based method to uncover the steering characteristics of a UAV. Firstly, a hybrid model of UAV was established, which included a dynamic model of articulated frames and a model of the hydraulic power steering system. Secondly, a field test of a typical UAV, a load-haul-dump (LHD) with 4 m3 capacity, was carried out. In order to verify the correctness of the established model and the accuracy of the involved parameters, the field test results were used to verify the dynamic model in time and frequency domains. Then, the steering characteristics of the UAV were uncovered based on the verified hybrid model, and the results showed that the increased load would increase ‘oversteering’ under the same articulation angle and that the error of trajectory exceeded 0.3 m. In addition, the deviations of trajectories between the two frames were revealed during the transient steering process, and the maximum deviation reached 0.21 m when the velocity was 2 m/s and the articulation angle was 15°. The comprehensive results indicate that the steering characteristics of UAVs cannot be ignored in regard to precise autonomous control and positioning.
In order to recover and utilize the potential energy of mining trucks efficiently, this paper proposes a nested optimization method of a novel energy storage system. By analyzing the multi-objective optimization problem of the oil-circulating hydro-pneumatic energy storage system, a nested optimization method based on the advanced adaptive Metamodel-based global optimization algorithm is carried out. Research shows that this method only requires a short time to solve the complex nonlinear hybrid optimization problem and achieves better results. The optimized energy storage system has higher system efficiency, energy density, and volume utilization rate, thus obtaining a smaller system volume and weight. Verified by the bench experiment of its powertrain, the hydro-pneumatic hybrid mining truck with the optimized energy storage system significantly reduces its fuel consumption and CO₂ emission. Thus, it lays the foundation for the practical application of hydro-pneumatic hybrid mining trucks.
Tire wear cost accounts for a large proportion of the total cost of heavy mining dump trucks (HMDTs), and the shimmy of the steering system aggravates the tire wear severely. This study proposes a model-based approach to avoid the shimmy of the steering system for such trucks without replacement or destruction of steering structure. First, a five degrees-of-freedom (DOF) shimmy dynamic model of the steering system is established considering the tire lateral dynamics and the nonlinearity of the hydro-pneumatic suspension (HPS). Second, the unstable parameter range of the dynamic model is obtained based on the Lyapunov’s first approximation theorem and Hopf bifurcation theory. The stability analysis results show that the steering system of heavy mining dump trucks is a self-excited vibration system because of the Hopf bifurcation in the unstable parameter range, and this unstable parameter range is greatly affected by the load and the initial pneumatic volume of hydro-pneumatic suspension. In addition, the accuracy of the dynamic is verified by a field test. Therefore, how the load and initial pneumatic volume affect the shimming is analyzed numerically. In other words, how to match the load and initial pneumatic volume is uncovered to avoid the shimmy. For instance, it shows that the shimmy at full load can be avoided at the speed of 30 km/h by charging the initial pneumatic volume of hydro-pneumatic suspension to 14.5 l.
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