The fault detection of the suspension system in a maglev train is of great importance for its operational safety and reliability. However, in random matrix theory (RMT), the size of the random matrix direct impacts the result of the mean spectral radius (MSR). In this paper, a state-of-the-art fault detection method for suspension systems is proposed using optimized RMT. The random matrix with the largest number of eigenvalues is obtained by reshaping the original data, with the help of the auto-correlation length from the correlation analysis. Finally, the optimized MSR is applied to detect the fault. The results of the experiment illustrate that the proposed method is applicable and effective.

This paper puts forth a strategy for sensorless finite-set model predictive thrust control (FS-MPTC) of linear induction motors (LIM) based on an improved extended Kalman filter (IEKF) observer. It is designed to address the shortcomings of existing LIM sensorless vector control methodologies, namely their lack of robustness and ineffective decoupling due to dynamic end effects. First, a fifth-order nonlinear discrete model of LIM is established in order to meet the requirements of sensorless FS-MPTC. An extended Kalman filter (EKF) observer based on the state-space equations is derived with the objective of achieving synchronous online observation of current, flux linkage and speed. In order to address the limitations of the traditional EKF, which employs a fixed system noise covariance matrix, this paper introduces an adaptive adjustment mechanism based on the difference between the theoretical predictions and actual measurements of the observer. This mechanism allows for the covariance matrix Q to be adjusted in real-time. The enhanced IEKF observer is then put forth. Simulation and experimental outcomes illustrate that, in comparison to the conventional EKF, the IEKF is capable of effectively adapting to internal noise fluctuations resulting from disparate operational conditions, system disturbances and LIM dynamic end effects. It is able to accurately estimate speed and flux linkage, operating stably under both high and low-speed conditions with commendable dynamic and robust performance.

Virtual Synchronous Generators (VSGs) can reduce the frequency and power oscillation in the grid. For parallel multi-VSGs in island microgrid, the differences of equivalent output impedance and line impedance affect the orderly sharing of reactive power and restraining of circulating current greatly. The power sharing and circulating current characteristics of VSGs are analyzed in this paper. To solve this problem, an optimization control method for parallel VSGs is proposed, which included the inner voltage and current loop and the outer reactive power loop. In the inner voltage and current loop, a virtual complex impedance including a resistive component and an inductive component is introduced. It reduces the coupling between active power and reactive power, and it reduces the disorderly distribution of power due to impedance differences. In the reactive power loop, the output voltage feedback and integrator links are adopted, which reduce the deviation of output voltage and restrain circulating current. The effects on the equivalent output impedance in the different control parameters and virtual complex impedance are analyzed, the effects on system stability in the different resistive components of virtual complex impedance are analyzed, and the proper parameters are selected. Simulation and experimental results show the correctness and validity of the proposed control method.

Traditional cascade control structures for surface-mounted permanent magnet synchronous motors (SPMSMs) suffer from strong coupling between the speed and current loops and high sensitivity to parameter variations. To address these issues, this paper proposes a model-free predictive direct speed control (MFPDSC) method based on an ultra-local model. The core innovation lies in constructing a direct speed ultra-local model that mathematically merges the q-axis current dynamics with the motion equation via integration, directly relating the rotor speed to the q-axis voltage. This eliminates the dependence on cascade structures and consolidates the disturbances of both loops into a single lumped perturbation for estimation. Compared with existing model-free predictive direct speed control schemes, the proposed method requires fewer control parameters. The d-axis adopts model-free predictive current control (MFPCC), and disturbances are compensated in real time by a data-driven observer and a reduced-order extended state observer (RESO). The d-axis current control and direct speed control act simultaneously to generate the voltage vector. Finally, experiments on a physical platform demonstrate that the proposed method offers advantages in speed tracking, load disturbance rejection, and robustness against parameter variations, thereby verifying the effectiveness of the proposed control strategy.

Switching active disturbance rejection control (SADRC) features a strong disturbance rejection capability and can be applied to speed control systems of permanent magnet synchronous motors. However, frequent switching of control modes in SADRC tends to cause fluctuations in motor speed. To address this issue, this paper proposes a composite control strategy that combines super twisting sliding mode control with switching active disturbance rejection control (STSM-SADRC). This strategy uses the STSM algorithm optimized by the hyperbolic tangent tanh function into both the extended state observer (ESO) and the state error feedback (SEF) of SADRC. The continuous and smooth property of tanh is adopted to replace the sign function, which suppresses the inherent chattering of the sliding mode while retaining the advantages of SADRC. The stability of the control algorithm is proved by the Lyapunov theorem. Simulation results show that it helps improve the tracking accuracy and disturbance rejection capability of the PMSM speed control system.

To address the issue of insufficient speed control robustness in the Linear Induction Motor (LIM) under end effects and load disturbances, this paper proposes a model-free error-adaptive sliding mode control strategy. This method compensates for lumped disturbances based on an Ultra Local Model (ULM) and Linear Extended State Observer (LESO) estimation and improves dynamic response and vibration suppression through error-adaptive gains. Simulation results show that the speed drop under a sudden 2000 N load is only 0.0227 m/s, and the disturbance rejection performance is superior to that of PI and Sliding-Mode Control (SMC).