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.

This paper mainly researches one method of speed and location detection for maglev vehicles. As the maglev train doesn’t have any physical contact with the rails, it has to use non-contact measuring methods. The technology based on the inductive loop-cable could fulfill the requirement by using an on-board antenna which could detect the alternating magnetic field produced by the loop-cable on rails. This paper introduces the structure of a speed and position system, and analyses the electromagnetic field produced by the loop-cable. The equivalent model of the loop-cable is given and the most suitable component of the magnetic flux density is selected. Then the paper also compares the alternating current (AC) resistance and the quality factor between two kinds of coils which the antenna is composed of. The effect of the rails to the signal receiving is also researched and then the structure of the coils is improved. Finally, considering the common-mode interference, 8-word coils are designed and analyzed.

This paper focuses on the optimal method of the non-contact velocity measurement and relative positioning system for medium and low speed maglev trains. By analyzing the working principle of the velocity measurement method based on counting sleepers and analyzing the speed system error and random error model, it is shown that the velocity measurement accuracy based on the counting sleeper speed measurement method needs to be further improved. Therefore, a method of using multi-sensor information fusion is proposed to improve the accuracy of velocity measurement and relative positioning. Firstly, aiming at the disturbance problem of traction, braking and suspension vibration in the attitude angle calculation and the cumulative error problem of attitude angle, a posture solution method combining the optimized second-order complementary filter and the velocity adaptation Unscented Kalman filter with maximum noise reduction is proposed; Then, in order to further reduce the accumulated error of the attitude angle and the high performance requirements of the gyroscope, the bias instability of the gyroscope is analyzed, and an adaptive wavelet de-noising algorithm based on threshold optimization is proposed; Finally, to weaken the colored noise interference caused by the suspension vibration and to weaken the velocity accumulation error, a fusion velocity measurement and positioning algorithm of multi-loop Kalman filter with acceleration fusion correction and velocity accumulation error correction is researched. The effectiveness of the proposed fusion method is verified through simulation comparison analysis and on-board engineering test. Compared with the velocity measurement method based on counting sleepers, the velocity measurement accuracy is improved by an order of magnitude, and its accuracy is comparable to the high-precision velocity measurement method based on the induction loop and GPS/INS. It has certain engineering applicability and application value.

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.

The magnetic suspension and balance system (MSBS) is a novel balance system that enables non-contact suspension of test models in wind tunnels, eliminating mechanical interference and providing more accurate aerodynamic data. However, the development of MSBS faces the critical challenge of designing a large-gap, non-contact multi-dimensional pose measurement system (MPMS) with both high accuracy and high frequency. This paper addresses this challenge through a unified co-design framework that seamlessly integrates three key innovations: (1) a real-time recursive pose feature extraction method using dynamic threshold and hysteresis comparison, (2) a nonlinear predictive filtering algorithm for signal enhancement, and (3) a synchronous parallel hardware architecture on FPGA. This integrated approach ensures that the entire processing chain - from signal acquisition to pose output - operates efficiently at 2 kHz while maintaining high accuracy. Experimental results demonstrate that the implemented MPMS achieves accuracy of 9.77 μm and 10.08m° in 5-DOF suspension tests, meeting the stringent requirements of practical wind tunnel applications.