Moving Horizon Estimation Sensorless Control for Linear Synchronous Motor in Maglev Train

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 linear synchronous motor test platform based on EMS maglev train is established to validate the proposed method. To finalize, this paper compares an extended Kalman filter, a modified MHE, and standard MHE 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.