Guodong Liang

Commercial truck path tracking is strongly affected by large mass and yaw moment of inertia, steering-rate constraints, and signal delays, whereas optimization-based predictive controllers can impose high online-computational costs. This study proposes a real-time path-tracking method based on Constraint-Handling Trajectory Prediction (CHTP). A dynamic model predicts the vehicle’s future pose, and a Stanley-based tracking law computes the desired steering angle from the predicted state. A constraint-handling module then explicitly limits the steering angle and its rate of change. The proposed CHTP method requires no online optimization. The method was evaluated through MATLAB/Simulink-TruckSim co-simulations and hardware-in-the-loop (HIL) tests. Under the low-speed unladen condition, CHTP reduced the maximum absolute-displacement error by 74.60% compared with the conventional Stanley controller. Under the high-speed unladen, low-speed heavy-load, and high-speed heavy-load conditions, CHTP completed the lane-change maneuver with bounded tracking errors, whereas the conventional Stanley controller failed to maintain convergent tracking. Across the four basic path-tracking conditions, the maximum absolute displacement and heading errors of CHTP did not exceed 0.3930 m and 0.1133 rad, respectively. Although nonlinear model predictive control (NMPC) generally achieved higher tracking accuracy, CHTP reduced the mean solution time by 94.17–95.96% relative to NMPC, with a maximum solution time of 1.1416 ms. Additional robustness tests showed that CHTP maintained bounded tracking errors and stable lateral dynamic responses under positioning errors, low road adhesion, and random response delays, while preserving its real-time computational performance. In the HIL test with a total loop delay of approximately 0.22 s, extending the prediction time from 0.20 s to 0.42 s limited the maximum displacement and heading errors to 0.2290 m and 0.1046 rad, respectively, with a maximum solution time of only 0.9895 ms. Additional prediction-time tests showed a trend consistent with the simulation results, further supporting the proposed delay-compensation mechanism. These results demonstrate that CHTP provides a favorable balance among tracking accuracy, robustness, and real-time performance.

High-gradient magnetic separation (HGMS) is a conventional and effective method for processing weak magnetic materials. A multi-field dynamic coupling simulation method integrating the Finite Element Method (FEM), Computational Fluid Dynamics (CFD), and the Discrete Element Method (DEM) was employed to investigate the separation behavior in HGMS. The dynamic deposition process of magnetic particles under the interactions of magnetic fields, fluid flow fields, and particle–particle forces was simulated using a two-way fluid–solid coupling algorithm based on the FEM-CFD-DEM coupling approach. Experimental results demonstrated that the particle deposition profiles predicted by the double-wire medium model were in good agreement with the measured data. The research findings indicated that the separation process could be divided into three distinct stages—the adsorption stage, the closure stage, and the clogging stage—each characterized by unique dynamic behaviors and pressure-drop evolution patterns. Additionally, the effects of key parameters such as the feeding velocity and medium filling ratio on the separation process were analyzed, providing theoretical foundations and technical support for the optimization of HGMS processes and the enhancement of separation efficiency.

Magnetic separation is an important method in the processing process, and its essence is the targeted dispersion of the mineral processing slurry pulp in the magnetic field space. The slurry is a complex multiphase fluid system with continuous phase carrying a large number of discrete phase particles, in which the magnetic particles agglomerate, migrate, and disperse under the dominance of magnetic force. In this process, there is nonlinear and unstable dynamic coupling between the continuous phase (liquid) and the discrete phase (solid particles) and between the discrete phases. In this paper, a dynamic cyclic multi-dipole magnetic moment algorithm with a higher calculation accuracy is innovatively proposed to calculate the magnetic interaction force between particles. Moreover, the P-E magnetization model suitable for a two-dimensional uniform magnetic field is further improved and optimized to make it applicable to a three-dimensional gradient magnetic field. Finally, based on the coupling of the Finite Element Method (FEM), Computational Fluid Dynamics (CFD), and Discrete Element Method (DEM), a dynamic coupling model capable of accurately simulating the magnetic separation process is developed. This model can be used to study the separation behavior of particles under a multiphase flow and multi-force field and to explore the motion behavior of magnetic particles.

Autonomous shovel digging of loaders is the key technology to realise automation and intelligent operation. The effective tracking control for the target operation trajectory is one of its core parts. Proportional–integral–derivative (PID) and other control methods without system models have issues, such as large overshoot amplitudes and jitter phenomena under system constraints. Given that model predictive control (MPC) effectively deals with system constraints to ensure smooth operation, this paper introduces MPC into motion control for the loader’s working mechanism and proposes a trajectory tracking control method based on nonlinear model predictive control (NMPC). This study shows that, under the same system constraints for different target operation trajectories, the designed controller achieves better tracking performance than conventional PID and sliding-mode control (SMC) controllers in handling system constraints and ensuring smoothness. It is also found that the tracking performance decreases as the dig insertion depth increases. Therefore, trajectories with larger dig insertion depths are not recommended as viable operation trajectories. This study provides an important foundation and new insights for improving the control performance of the loader’s working mechanism.

Achieving precise load detection for Intelligent Loaders is an important task, which directly affects the operation energy efficiency and the fatigue life analysis for the loader’s working mechanism. The operation of the mechanism is regarded as a 3-DOF (degree of freedom) planar motion process coordinated with the vehicle body. Affected by complex dynamic coupling in motion, the existing dynamic models of the mechanism have the problem of insufficient accuracy, which is not conducive to the precise calculation of load. Taking the reverse six-linkage loader as the research object, an accurate dynamic model of the mechanism is established considering its cooperative motion with the vehicle body. Firstly, the kinematic description of the mechanism is given by the Rodriguez method. Then, to overcome the coupling effect caused by the cooperative motion, the sufficient inertia forces of the mechanism are established in joint space using the Lagrange method. Furthermore, to overcome the coupling effect caused by the complex structure, the Newton–Euler method is used to establish the force mapping relations between the joint space and the drive space by multi-body modeling. Finally, the dynamic model of the mechanism in drive space is obtained, and the specific mapping relations between the bucket force, the vehicle driving force, and the drive parameters are given. Compared with existing dynamic models in simulation, the analysis shows that the average and maximum absolute errors of the vehicle driving force calculated by the established model do not exceed 20% of the existing model errors, and the corresponding errors of the bucket force do not exceed 10% of the existing model errors, which proves that the motions of vehicle body and front-end mechanism, as well as the force of the tilt hydraulic cylinder, play important roles in improving the model accuracy. The established model is superior to existing models and is more suitable for cooperative motion with the vehicle body.

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. The design process of this control method includes establishing the NMPC controller based on the time-varying local model, generating learning samples based on this NMPC controller, and training to obtain the neural network controller. The proposed controller is tested by a joint simulation of MATLAB and Carsim and compared with other controllers. According to the simulation results, the accuracy of the NN controller is close to that of the NMPC controller and far better than that of the Stanley controller. In all simulations, the absolute value of displacement error of the NN controller does not exceed 0.2854 m, and the absolute value of heading error does not exceed 0.2279 rad. In addition, the real-time performance of the NN controller is better than that of the NMPC controller. The maximum time cost and average time cost of the NN controller are, respectively, 40.91% and 22.37% smaller than those of the NMPC controller under the same conditions.

Autonomous shoveling of loaders is the key technology to realize automatic and intelligent operation. Tracking control for target trajectory during shoveling is one of its core technologies. The actual motion trajectory of bucket in the pile directly affects the production, resistance force and energy consumption of the shoveling operation, so it is of great significance to achieve high-precision tracking control. Traditional PID control method has problems such as overshoot. To solve these problems, this paper first establishes a kinematic model of the working mechanism in drive space, then introduces the preview control method, combines PID with preview control, and designs a PID controller with preview distance based on kinematic decoupling, which improves the control performance by judging the future trajectory information in advance. The ADAMS/Simulink co-simulation shows that compared with traditional PID, the PID controller with preview distance improves the overshoot of bucket during steering and reduces the displacement error and angle error of bucket during tracking. Therefore, PID control with preview distance can comprehensively improve the control performance of traditional PID. In addition, it is found that how to choose a suitable preview distance is also a key factor to be considered in future work.

Automatically and accurately obtaining payload information from Load-Haul-Dump (LHD) vehicles is a typical application of information and intelligence technology in engineering equipment, which provides an important support for efficient and safe construction. In this paper, a novel comprehensive weighing model for LHD with high accuracy and adaptability is proposed by Newton-Euler method. The accuracy of the model has been greatly improved with the consideration of some critical factors such as materials randomness, driving cylinders coupling, dynamic characteristics of mechanism, system damping and working pavement. The effect of the critical factors on the accuracy of weighing model is deeply analysed through significance analysis and comparison tests, which provides an important basis for the improvement of weighing model. Validation shows that the novel model is superior to traditional models in accuracy and adaptability. In theory, the accuracy of novel model is at least 3 times higher than that of traditional models, and its accuracy can still achieve ±0.3% in complex velocity or acceleration motion, and the larger the load mass, the higher the accuracy. As a result, the novel comprehensive weighing model can provide accurate load information for LHD vehicles without affecting working efficiency. It is of great theory and application value to the development of information and intelligence technology in engineering equipment and contributes to efficient and safe construction.

Interval estimation of aircraft fuel consumption is an important basis for airlines to make flight plans and to save energy and reduce emissions. Aiming at the problem that the fuel consumption samples under the influence of multiple factors presented irregular distribution, which would reduce the rationality and accuracy of fuel consumption estimation, an interval estimation method of trip fuel consumption based on the combination of support vector quantile regression and Bootstrap (SVQR-Bootstrap) was proposed. The method based on quantile regression residual error minimization of the absolute value of asymmetric form thoughts to obtain the conditional quantile of trip fuel consumption and on this basis, through the Bootstrap estimate mean and standard deviation of trip fuel consumption population, and then, according to fixed aircraft type and segment, the population of fuel consumption meets the normal distribution, building estimated interval of trip fuel consumption under a constant confidence level. According to simulation comparisons, the SVQR-Bootstrap method can effectively reduce the length of the estimation range, pointing to the clear, which is better than the interval estimation method which requires assumed data distribution.

We propose an optimal planning scheme of the bucket trajectory in the LHD (Load-Haul-Dump) automatic shoveling system to improve the effectiveness of the scooping operation. The research involves simulation of four typical shoveling methods, optimization of the scooping trajectory, establishment of a reaction force model in the scooping process and determination of optimal trajectory. Firstly, we compared the one-step, step-by-step, excavation and coordinated shoveling method by the Engineering Discrete Element Method (EDEM) simulation. The coordinated shoveling method becomes the best choice on account of its best comprehensive performance among the four methods. Based on the coordinated shoveling method, the shape of the optimized trajectory can be roughly determined. Then, we established a model of bucket force during the shoveling process by applying Coulomb’s passive earth pressure theory for the purpose of calculating energy consumption. The trajectory is finally determined through optimizing the minimum energy consumption in theory. The theoretical value is verified by the EDEM simulation.