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.

When the electromagnetic suspension (EMS) type maglev vehicle is traveling over a track, the airgap must be maintained between the electromagnet and the track to prevent contact with that track. Because of the open-loop instability of the EMS system, the current must be actively controlled to maintain the target airgap. However, the maglev system suffers from the strong nonlinearity, force saturation, track flexibility, and feedback signals with network time-delay, hence making the controller design even more difficult. In this article, the minimum levitation unit of the maglev vehicle system has been established. An amplitude saturation controller (ASC), which can ensure the generation of only saturated unidirectional attractive force, is thus proposed. The stability and convergence of the closed-loop signals are proven based on the Lyapunov method. Subsequently, ASC is improved based on the radial basis function neural networks, and a neural network-based supervisor controller (NNBSC) is thus designed. The ASC plays the main role in the initial stage. As the neural network learns the control trend, it will gradually transition to the neural network controller. Simulation results are provided to illustrate the specific merit of the NNBSC. The hardware experimental results of a full-scale IoT EMS maglev train are included to validate the effectiveness and robustness of the presented control method as regards to time delay.

The vertical security problem of maglev train is challenging for nonlinearity, external disturbances, unmeasurable airgap velocity and constrained output. To solve this problem, a semi-supervised controller based on deep belief network (DBN) algorithm is proposed in the presence of unknown external disturbances. Firstly, the extended state observer (ESO) is designed to ensure fast convergence of observation errors with high enough estimation precision. An output-constrained controller is designed by backstepping method, and the estimated value of ESO is introduced to ensure that the output airgap is constrained within a bounded range. Then, the stability of this method is proved based on the symmetric Barrier Lyapunov function. Subsequently, a semi-supervised controller is presented based on DBN algorithm and the output-constrained controller. The numerical simulation results show that this method can effectively deal with unmeasurable airgap velocity and generalized external disturbances, and guarantee the vertical security with output airgap within a bounded range. Finally, experiments are implemented on a full-scale maglev vehicle and the experimental results demonstrate that the developed deep learning controller can ensure the vertical security.

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 running environment of a magnetic levitation (maglev) vehicle is complex, with problems such as track irregularity, external disturbance, varying system parameters, and time delays, which bring great challenges to the design of a high-performance levitation controller. In this article, an adaptive neural network controller with input delay compensation and a control parameter optimization scheme is proposed for the electromagnetic levitation system of a maglev vehicle, which can solve the key engineering problems of external disturbance, input time delay, and time-varying mass. Aiming at the problem of input time delay, a sliding-mode surface with time-delay compensation is constructed, and a double-layer neural network and adaptive law are utilized to approximate the uncertain dynamics; thus, a finite time adaptive tracking control law is proposed. Based on the Lyapunov method, the stability of the proposed controller in finite time is analyzed. The proposed method does not only update the input and output weights of the neural network online, but also introduces reinforcement learning (Actor–Critic network) to optimize the key controller parameter in real time and enhance system robustness. Simulation and experimental results show that the proposed controller can effectively suppress the air gap vibration with time delay and uncertain dynamics, and significantly improve the performance of levitation control.