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.

The electromagnet levitation system (ELS) of low-speed maglev train is taken as the research object. The nonlinear dynamics and control law of ELS are discussed. Specifically, by employing the Euler-Lagrange’s method, a nonlinear dynamic model is constructed for the single-ELS. Then, the linear control law is studied, which has a disadvantage of weak robustness. To improve the performance of the controller, a fuzzy sliding-mode control law is proposed. According to the dynamic nonlinear model, a novel sliding surface which can make the system reach the stable point within the finite time is presented. Moreover, the fuzzy inference method is utilized to slow down the speed of the states crossing the sliding surface. The simulation results demonstrate that the global robustness of external disturbance and parameter perturbation can be achieved through the proposed control law. And the chattering phenomenon can be reduced significantly. Finally, the experiments are also implemented to examine its practical dynamic performance of the proposed control law.

The effects of curvature ratios on heat transfer and flow characteristics based on multi-sensor technique for the multi-layer winding hoses are studied. A multi-sensor–based experimental platform is established. The working fluid is oil and the multi-layer winding hoses are produced by winding a 13.00 mm diameter hydraulic hose on a reel from 1 to 12 turns. Experiments on different curvature ratios for the multi-layer winding hose with constant wall temperature are presented. A_k–ε standard model has been applied to present the simulations on heat transfer and turbulent flow. In order to solve this model, a finite volume method has been used. The simulation results are compared with the experimental results. The simulation results and experimental results are in the similar varying trends. The effects of centrifugal force in the multi-layer winding hose on heat transfer and pressure drop have been discussed.