Peichang Yu

The Active Control of Maglev Stationary Self-Excited Vibration With a Virtual Energy Harvester

This paper addresses the active control of stationary self-excited vibration, which degrades the stability of the levitation control, decreases the ride comfort, and restricts the construction cost of the maglev system. First, a minimum interaction model containing a flexible bridge and a single levitation unit is presented. Based on the minimum interaction model, the principle underlying the self-excited vibration is explored. It shows that the active property of the levitation system is the root of self-excited vibration. Consider that the energy of vibration may be absorbed by the electromagnetic energy harvester (EEH), so that a technique applying it to the bridge is proposed, and the stability of the combined system is analyzed. However, its hardware structure is complicated, and the cost of construction is prohibitive. Then the novel conception of the virtual EEH is brought forward, which uses the electromagnetic force to emulate the force of a real energy harvester acting on the bridge. With the estimation of the vertical velocity of the bridge and the frequency of vibration, the self-oscillatory is avoided as well by adding an extra control instruction to the electromagnet. After building the overall dynamic model with details, numerical simulations and field experiments are carried out, and the results illustrating the improvement of stability are provided and analyzed.

The Active Fractional Order Control for Maglev Suspension System

Maglev suspension system is the core part of maglev train. In the practical application, the load uncertainties, inherent nonlinearity, and misalignment between sensors and actuators are the main issues that should be solved carefully. In order to design a suitable controller, the attention is paid to the fractional order controller. Firstly, the mathematical model of a single electromagnetic suspension unit is derived. Then, considering the limitation of the traditional PD controller adaptation, the fractional order controller is developed to obtain more excellent suspension specifications and robust performance. In reality, the nonlinearity affects the structure and the precision of the model after linearization, which will degrade the dynamic performance. So, a fractional order controller is addressed to eliminate the disturbance by adjusting the parameters which are added by the fractional order controller. Furthermore, the controller based on LQR is employed to compare with the fractional order controller. Finally, the performance of them is discussed by simulation. The results illustrated the validity of the fractional order controller.

Suspension system status detection of maglev train based on machine learning using levitation sensors

The objective of this paper is to design a new method, based on machine learning, to detect the abnormal status of the suspension system of maglev train by using the real-time signals collected by the levitation sensors. The abnormal status is harmful to the operation of maglev train. It is necessary to detect the abnormity timely. Generally, the operation data of train is recorded online, but it is analyzed by experts off-line, the efficiency of this approach is very low. So, an efficient and accurate method is urgently demanded. There are 120 levitation sensors in a carriage, which measure the levitation states with nearly 40khz sampling frequency. In tradition, the information of sensors is just used as the feedback of suspension controllers. This paper will study how to use these information for detecting the abnormal status of suspension system by using support vertical machine (SVM). Firstly, some reasonable features originate from experience are extracted from levitation sensors. Secondly, considering that the fault rate is very low, resulting in less fault samples. So, the SVM method is introduced. At last, the raised machine learning model is applied in the practical usage on maglev train by using the programming in the Digital signal processor (DSP) controller. By this method, the existing abnormal status of suspension system can be diagnosed in real-time. The experiment results show that this algorithm can achieve the detection accuracy of 91.3%.

Response and control of a levitation module under track irregularities when the maglev train is traveling

The EMS (Electro-Magnetic Suspension) maglev train uses attractive magnetic forces to neutralize its weight, which puts a rather high requirement on the smoothness of the track since the levitation gap is generally 8∼10 mm. It has been observed that in a traveling urban maglev train, the levitation gap error of the rear levitation unit in a levitation module is generally more prominent than that of the front levitation units; and under poor track conditions, the rear electromagnet of the levitation unit is more likely to clash with the track and to cause a levitation failure. However, this phenomenon has not yet been well interpreted. In this paper, a model of the levitation module in a maglev train is built to study the responses of the levitation gaps under different vehicle speed and track conditions, and results show that the levitation module is sensitive to some specified wavelengths of the track irregularities, and the analysis well interprets the reason that causes the difference of the response for the front and the rear levitation unit under track irregularities. A control strategy is also proposed to reduce the rear gap fluctuation.

An performance assessment method for suspension control system of maglev train

The objective of this study is to investigate a practical performance assessment method for the control system in maglev train based on its operation data. Firstly, several features related to performance assessment are extracted with the analysis of the suspension system mathematical model. Considering that the vehicle operation conditions are complex and there are many uncertain disturbances, the assessment process of suspension system performance is studied by using support vector machine and clustering based on operating data. Further more, experiments are conducted on maglev train CMS04, the suspension system performances are evaluated at different working conditions, and the results proved the correctness of the assessment method. At last, to further system improvement, some experiments are carried out for the research of optimal parameters in the ground of results calculated by the raised assessment method.