The dynamic interaction of vehicles/bridges is an important part of the research field of the Electromagnet Suspension (EMS) maglev train. However, multiple factors affect the dynamics of the coupling system, which increases the complexity of the vehicle-bridge coupling system, and makes it difficult to analyze. Herein, combining the advantages of theoretical derivation and numerical calculation, a fast and accurate analysis method of maglev vehicle and bridge system is proposed to analyze the influence of bridge and controller on the dynamics of the maglev system. This work is directed to the Changsha maglev express line, in which the mathematical equations for the vehicle- bridge system is presented. Then the influence of the parameter on the system is analyzed. Finally, the vibration experimental test system is set up, and the vibration test of the maglev vehicle bridge is tested by the test scheme, and the experimental results data are given. The test results of vibration acceleration are in good agreement with the calculation results of the proposed method, which verifies the accuracy of the model and the analysis method. Therefore, the research in this paper can provide theoretical guidance for Maglev engineering, and the analysis method in this paper can also be used in the study of other vehicles, such as the high-speed maglev train.

In the maglev transportation system, the track beam may vibrate under the dynamic levitation force of the maglev vehicle due to its own elastic characteristics, which may cause the vehicle to vibrate. At present, the general method of suppressing vibration is to improve the stiffness, density, damping of the bridge, and the accuracy of track installation, which will result in higher construction costs. In this article, the simulation analysis of the vehicle-rail coupling vibration is carried out from the perspective of suspension control characteristics. Through theoretical derivation, a mathematical model is established, and the mechanism of the vehicle-bridge coupling system vibration is analyzed through simulation. The internal influence of the main parameters of the controller on the system vibration is discussed. Theoretical analysis and simulation research show that the mismatch between the structural parameters of the track beam and the controller parameters will cause coupled vibration, while adjusting the parameters to match the track beam structure can suppress vibration to a certain extent. Taking the 25-m simply supported beam of Changsha Maglev Express as the research object, the vehicle-bridge coupled vibration characteristics test under 75–140-km/h operating speed is carried out, and the dynamic response of the vehicle and the bridge frame is obtained. Dynamic experiments show that proper controller parameters can maintain a stable suspension of the vehicle. This research provides theoretical and experimental references for vehicle-track coupled vibration suppression and maglev transportation system optimization.

Magnetic levitation has been applied to maglev trains and magnetic suspension wind tunnel. However, there are some problems with the existing levitation control. For example, it is difficult to extract smooth velocity signals from the gap sensor with noise. The classical differentiator is susceptible to noise, which makes the levitation system sometimes vibrate. The accuracy and stability of levitation control need to be improved. The velocity fusion estimation method (VFE) is proposed to extract the velocity signal from the gap and acceleration sensor, which is theoretically derived to prove that it reduced the noise of the velocity signal. Then disturbance rejection control(DRC) is proposed that add VFE into classical levitation control. Because of the high-quality velocity signal and accelerometer’s fast responsiveness, it makes DRC have many advantages. The advantages of using the proposed control structure are that it improved the control accuracy of the target gap and resisted disturbance effectively. Air-gap fluctuations in levitation systems can be reduced in transient pulse disturbance test, white noise disturbance test, and external force disturbance test when it applies the proposed control method. The effectiveness and advantages of the proposed control method are verified by the simulation and experiments.

To address the challenging requirements of citrus detection in complex orchard environments, this paper proposes ACDNet (Adaptive Citrus Detection Network), a novel deep learning framework specifically designed for automated citrus harvesting. The proposed method introduces three key innovations: (1) Citrus-Adaptive Feature Extraction (CAFE) module that combines fruit-aware partial convolution with illumination-adaptive attention mechanisms to enhance feature representation with improved efficiency; (2) Dynamic Multi-Scale Sampling (DMS) operator that adaptively focuses sampling points on fruit regions while suppressing background interference through content-aware offset generation; and (3) Fruit-Shape Aware IoU (FSA-IoU) loss function that incorporates citrus morphological priors and occlusion patterns to improve localization accuracy. Extensive experiments on our newly constructed CitrusSet dataset, which comprises 2887 images capturing diverse lighting conditions, occlusion levels, and fruit overlapping scenarios, demonstrate that ACDNet achieves superior performance with mAP@0.5 of 97.5%, precision of 92.1%, and recall of 92.8%, while maintaining real-time inference at 55.6 FPS. Compared to the baseline YOLOv8n model, ACDNet achieves improvements of 1.7%, 3.4%, and 3.6% in mAP@0.5, precision, and recall, respectively, while reducing model parameters by 11% (to 2.67 M) and computational cost by 20% (to 6.5 G FLOPs), making it highly suitable for deployment in resource-constrained robotic harvesting systems. However, the current study is primarily validated on citrus fruits, and future work will focus on extending ACDNet to other spherical fruits and exploring its generalization under extreme weather conditions.

The research on the dynamic characteristics of vehicles requires wind tunnel tests. The natural mass frequency of the support mechanism and the interference of the support mechanism on the flow field will greatly reduce the repeatability and accuracy of the experimental results. The magnetic suspension and balance system (MSBS) uses non-contact electromagnetic force instead of mechanical support to eliminate the support mechanism’s interference with the test results. The MSBS needs to actively adjust the electromagnetic force to control the position of the tested model. At present, its control strategy is PID control. However, when the wind tunnel is opened, there will be large aerodynamic loads, leading to large changes in displacement. We propose a disturbance compensation control algorithm framework that can quickly estimate the external force disturbance and quickly compensate it to the control loop, reduce the influence of the disturbance on the control system, and provide stability and accuracy of displacement control.

The magnetic suspension wind tunnel balance (MSBS) is an entirely new device for aerodynamic measurement, and it makes the best of the electromagnetic force to suspend the aircraft model in the wind tunnel without contact. Compared with conventional wind tunnel balance, it absolutely abandons the model support and airflow interference. Therefore, the aerodynamic measurement environment is more authentic and the aerodynamic measurement results are more accurate. The electromagnetic field in MSBS plays a major role in bearing the force of wind. The numerical computation and finite element numerical analysis are performed to investigate key factors of electromagnetic force under different conditions. The calculation results based on finite element method (FEM) have revealed that the diameter and the spacing of of the axial coil, the number of segments and the pitch angle of the suspension model are key factors of electromagnetic force. Based on the above key factors, the structure of the magnetic suspension balance is optimized to maximize the electromagnetic force under multiple constraints.