The magnetic suspension system of low-speed maglev train in National Maglev Transportation Engineering R&D center is taken as the object of study. The design and realization of magnetic suspension controller is discussed. The nonlinear mathematical model of the magnetic suspension system is built. Then, the PID controller is investigated, which indicates the PID controller is sensitive to disturbance. In order to reject the disturbance and parameter perturbations, an adaptive neural-fuzzy sliding mode controller (ANFSMC) is presented, which employs sliding mode control, adaptive-fuzzy approximator and neural-fuzzy switching law. The sufficient simulation and experimental results are included to demonstrate that the presented robust controller significantly reduces the impact of the disturbance and parameter perturbations with smooth control current.

The maglev rail transit has entered a rapid development stage. In order to prevent potential safety hazards in the operation of maglev train, the related monitoring technology needs to be studied urgently. In view of the wide application of the internet of things (IoT) in intelligent transportation, a new method for realizing suspension control for medium-low-speed maglev trains using the IoT and an adaptive fuzzy controller is proposed. Firstly, a mathematical model of the suspension system of medium-low-speed maglev trains is established. Then, the basic composition of the IoT and the circuit design of the key components of maglev trains are introduced. On this basis, an improved Apriori algorithm is used to extract the stored historical database and establish a trusted database. Then, according to the data of the trusted database, the suspension airgap control law is extracted, and the adaptive fuzzy rules of the maglev train suspension system are determined. An improved adaptive suspension controller is designed. Finally, the effectiveness of the method is verified by experiments utilizing a full-scale maglev train.

As new advanced vehicles, electromagnetic suspension (EMS)-type maglev trains have received wide attention because of their advantages such as high speed, no mechanical friction, low noise, low cost and energy consumption, strong climbing ability, and green environmental protection. The open-loop instability is one of the key points and difficulties for the levitation control systems of maglev trains. The closed-loop feedback control method must be applied to realize stable levitation. However, there are currently many levitation control methods just in theory. Considering their advantages and disadvantages, it is a major demand for maglev trains to select efficient, stable, applicable, and cost-saving methods to improve their dynamic performance and safety, which motivated this review. First, the current status of research on maglev trains is introduced in this paper, including types, system components, and research modes in various countries, followed by an analysis of the levitation control methods for EMS-type maglev trains. Then, the technical characteristics of the levitation control systems are described according to the basic principles of levitation systems, model building, mathematical derivation, and control objectives. Next, three kinds of typical levitation control methods are reviewed, namely, linear state feedback methods, nonlinear control methods, and intelligent control methods, according to their improvements and applications. Lastly, we summarize and evaluate the advantages and disadvantages of the three methods, and future developments of levitation control are suggested.

In order to ensure the suspension stability of maglev train, the active control problems of static suspension and dynamic suspension are studied. First, the mathematical model of dynamic suspension characteristics of medium- and low-speed maglev train is established. Second, the PID controller is studied. The results show that the controller is very sensitive to time-varying disturbances such as nonuniformity and load, which leads to the decrease in system robustness. In order to suppress the disturbance effectively, a suspension control method based on the sliding mode reaching law is proposed. Based on the bifurcation phenomenon, the bifurcation point is solved to determine the main control parameters. On the basis of this, an acceleration feedback correction and adaptive control module based on the radial basis function (RBF) network is constructed to effectively suppress the vibration of the electromagnet. The controller consists of a sliding mode bifurcation control law, an acceleration feedback correction module, and an adaptive compensation loop. The simulation and experimental results show that the proposed control algorithm can significantly reduce the variation range of suspension gap with a smoother control current in the presence of complex disturbances and significantly suppress the coupled vibration.

Since the high-speed maglev line in Shanghai was put into operation in 2003, it has been running safely for 15 years, maintaining the highest speed of public ground traffic 430 km/h and 99.8~99.9% punctuality. Since 2001, China ministry of science and technology (MOST) has been supporting the research and development of high speed maglev transportation technology. In 2016, under the support of the MOST, China Railway Rolling stock Corporation (CRRC) started to lead the program of engineering research of Maglev. The aim of the program is to develop 600 km/h high speed Maglev system and 200 km/h medium speed maglev system. The newly developed high speed Maglev system could be tested, verified and applied on the planned Shanghai - Hangzhou high speed Maglev line. Since the operation opening of Changsha Maglev Airport Express Line in May 2016, Hunan province has planned to build more maglev line with top speed 100–160 km/h. Following the operation of urban maglev in Changsha and Beijing, the Qingyuan Maglev line in Guangdong province began to build at the end of 2017. The new maglev line will connect hot spring tourist attractions and Changlong theme park (animal park) in 2019. This paper introduces the application and construction of maglev transportation in China, recent R&D status, further plans and trend of development.

Maglev vehicles have become a new type of transportation system with higher speed, lower noise, and commercial appeal. Magnetic-suspension systems, which have high nonlinearity and open-loop instability, are the core components of maglev vehicles. The high-performance control of maglev vehicles has been the focus of numerous studies. Encountering challenges in the levitation control of maglev vehicles in the form of uncertain time delays and disturbances is unavoidable. To cope with these problems, this study presents the design of an adaptive robust controller based on the Riccati method and sliding-mode technology, simultaneously taking into account the influence of time delays and disturbances. The asymptotic stability of the closed-loop system with the proposed control law is proved by the Lyapunov method. Control performances of the proposed controller are shown in the simulation results. Together with the consistently stabilizing outputs, the presented control approach can handle time delays and disturbances well. Finally, experiments were also implemented to examine its practical control performance of the robust levitation-control law.