Track irregularities, caused by reasons such as track deformation and installation error, are common in real maglev line. These disturbances brought by tracks exert an adverse influence on the performance of a maglev train levitation system. It takes a lot of maintenance costs and time to keep the tracks in a good condition. In another way, a disturbance rejection through a controller optimization method is proposed in this paper. First, the influences of the track irregularity disturbances on levitation performance are illustrated in detail. Then, an online optimization of the PnP control architecture method is adopted to reject the disturbances caused by track irregularities. The effectiveness of this method is verified through the MATLAB simulation. This method makes the levitation system adaptive to known and unknown time-varying track irregularities, saving time, and human resources.

To improve the riding performance and levitation stability of a high-speed permanent magnet electromagnetic suspension system maglev train, a control strategy based on an integral joint structure model is proposed. First, the system model of joint structure instead of a single maglev unit is established, then a state observer is designed to observe the movement of a suspension frame, and a control strategy based on linear quadratic optimal control method is proposed. This control strategy based on a joint structure model is advantageous in suspension performance in the presence of external disturbances. Comparative simulation results show that this control strategy has a better performance under the condition of external force disturbance. An experiment on single-carriage high-speed permanent magnet electromagnetic suspension system maglev train is conducted on a 1.5-km test line; the performance is satisfactory for employment.

In a maglev train levitation system, signal processing plays an important role for the reason that some sensor signals are prone to be corrupted by noise due to the harsh installation and operation environment of sensors and some signals cannot be acquired directly via sensors. Based on these concerns, an architecture based on a new type of nonlinear second-order discrete tracking differentiator is proposed. The function of this signal processing architecture includes filtering signal noise and acquiring needed signals for levitation purposes. The proposed tracking differentiator possesses the advantages of quick convergence, no fluttering, and simple calculation. Tracking differentiator’s frequency characteristics at different parameter values are studied in this paper. The performance of this new type of tracking differentiator is tested in a MATLAB simulation and this tracking-differentiator is implemented in Very-High-Speed Integrated Circuit Hardware Description Language (VHDL). In the end, experiments are conducted separately on a test board and a maglev train model. Simulation and experiment results show that the performance of this novel signal processing architecture can fulfill the real system requirement.

Although the canonical correlation analysis (CCA)-based method has been successfully applied to Gaussian processes, the methods cannot obtain good results for the non-Gaussian processes. Meanwhile, due to the different operating conditions of the systems in actual operation, the data generated under different operating conditions is greatly different, which makes the fault detection more difficult. To deal with these problems, a fault detection method based on multiple canonical correlation analysis models and Box-Cox transformation is proposed. The method divides all conditions into some categories of conditions and builds the switching rules. Then the current condition is determined and switched in real time through real-time status data and switching rules. Since the systems are the non-Gaussian processes, the quadratic statistic established by the CCA for each condition is a non-negative and non-Gaussian distributed variable. So, Box-Cox transformation is used to convert quadratic statistic into Gaussian distributed variables, such that the properties of Gaussian distribution can be employed to determine the ranges of the quadratic statistic corresponding to different health conditions. Finally, the fault detecting thresholds of the models are established based on a 99.7.