Thrust Bandwidth Modeling and Optimization of PMSLM Based on Analytic Kernel-Embedded Elastic-Net Regression

Abstract

To improve the bandwidth and thrust performance of permanent magnet synchronous linear motor (PMSLM), from the perspective of motor structure design, this paper proposes a high-accuracy and fast-calculation inductance model of centralized winding based on analytic kernel-embedded Elastic-Net regression (AKER) to establish a comprehensive analytical model of PMSLM performance. A stochastic fractal search (SFS) algorithm is introduced to optimize the comprehensive performances of PMSLM. First, Neumann's formula of integral is used to establish the equivalent inductance analytical model (EIAM) for the three-phase winding of PMSLM, and the analytic kernel is constructed based on the EIAM. In combination with the finite element simulation samples of inductance, the analytical kernel is embedded into the Elastic-Net regression to establish the inductance model of high efficiency, namely AKER, and compared with the finite element mode (FEM), EIAM and other advanced methods, the superiority of AKER is verified from the accuracy and time cost. Then, based on the proposed AKER inductance model, the current and voltage equations of PMSLM are decoupled in the two-phase rotating coordinate system to establish the comprehensive analysis model of the PMSLM performance. Based on the comprehensive model, the optimization function of PMSLM is constructed, and the SFS algorithm is introduced to solve the function iteratively. Finally, the effectiveness of the proposed modeling and optimization method is verified by FEM-based control simulation and prototype experiment respectively.