Optimal Reference Primary Flux Based Model Predictive Control of Linear Induction Machine With MTPA and Field-Weakening Operations for Urban Transit

Abstract

The linear induction machine (LIM) in urban transit suffers from poor efficiency and thrust decay due to the large air-gap length, longitudinal end effect, and the light load operating conditions. Maximum thrust per ampere (MTPA) is one of the effective methods to improve the motor efficiency by minimizing the conduction loss. However, when MTPA is introduced in model predictive control as an additional constraint, traditional methods inevitably need to add more weighting factors in the cost function. To further improve the performance of LIM drives and eliminate the weighting factors, this article proposes an optimal reference primary flux based model predictive control (ORPF-MPC) for LIM. The ORPF containing MTPA condition is derived and introduced as the only criterion in cost function to achieve the control goal without using any weighting factors. Furthermore, the ORPF is extended to the field-weakening operation, which significantly widens the operating range and nearly achieves the optimal thrust capability to mitigate the thrust decay of LIM. Finally, the proposed ORPF-MPC is implemented with simplified arbitrary double vectors to remove the restriction on the selection of voltage vector pairs and further reduce the flux and thrust ripples. The proposed method is comprehensively investigated on one 3 kW arc induction machine, and its effectiveness is fully validated through both simulation and experimental results.