The speed profile tracking calculation of high-speed maglev trains is mainly affected by running resistance. In order to reduce the adverse effects and improve tracking accuracy, this paper presents a maglev train operation control method based on a fractional-order sliding mode adaptive and diagonal recurrent neural network (FSMA-DRNN). First, the kinematic resistance equation is established due to the three types of resistance that occur during the actual operation of a train: air resistance, guide eddy current resistance, and suspension frame generator coil resistance. Then, the FSMA-DRNN control law and parameter update law are designed, and a FSMA-DRNN operation controller is composed of three parts: speed feed forward, fractional-order sliding mode adaptive equivalent control, and diagonal recurrent neural network resistance compensation. Furthermore, by using the designed operation controller, it is proven effective by the Lyapunov theory for the stability of the closed-loop control system. Apart from the proposed theoretical analysis, the proposed approaches are verified by experiments on the high-speed maglev hardware-in-the-loop simulation platform Rt-Lab, in line with the 29.86 km test line and a five-car train from the Shanghai maglev, showing the effectiveness and superiority for operation optimization.

Rail fastener is a crucial component equipment to ensure the safe operation of the train, and it is very paramount to detect the loose state of the fastener. In this paper, the vertical vibration acceleration signal of wheelset is taken as the research object, and the loose state of fastener is identified by separating and calculating the key IMF energy entropy. Firstly, based on the finite element theory and the principle of multibody dynamics, the rigid-flexible coupling simulation model of vehicle track is established. Then, the vertical vibration acceleration signals of the wheelset under the speed of 200 km/h are obtained by setting the different loosening degrees of the fastener. Finally, we use optimized HHT to process signals, and the orthogonal empirical mode decomposition method (OEMD) is proposed to optimize the orthogonality of the intrinsic mode function, to eliminate the IMF component having poor correlation with the original signal; the Hilbert time spectrum and information entropy theory are combined to calculate the energy entropy of the key IMF, and the HHT energy entropy evaluation algorithm of the vertical acceleration response signal of the train wheelset is proposed. The simulation results show that the HHT energy entropy of 100% fastener looseness is less than 25%, 50%, and 75%, decreasing trend. The algorithm can recognize the looseness of track fastener through the experiment under different working conditions.

This article presents a novel position and speed estimation strategy based on moving horizon estimation (MHE) utilized in the linear synchronous motor (LSM) drives for electromagnetic suspension (EMS) high-speed maglev train. The mathematical models for LSM considering lumped disturbances are established. In light of this, an optimization driven MHE method that excels in performance, is put to use for estimating speed and position. Given lenient assumptions, an optimal solution to a quadratic program with equality constraints is computed in each cycle. Besides, the lumped disturbances observer was derived and constructed, characterizing residual perturbation at each iteration of the moving window estimation. Moreover, a proportional scaling LSM test platform based on EMS maglev train is established to validate the proposed method. To finalize, this article compares an extended Kalman filter (EKF), a modified MHE (mMHE), and standard MHE (sMHE)-based sensorless control methods. These proposal’s steady state and transient state are tested with respect to different traction conditions, and estimation error and computational cost are analyzed. The results prove that the proposed scheme achieves superior in estimation precision while running in a real-time control environment, even in low-speed range.