Speed Sensorless Model Predictive Current Control of Linear Induction ‎Machines in Urban Transit

Abstract

There is an increasing interest in achieving global goals of mitigating climate changes that target environment protection. Thus, electric vehicles (as linear metros) were elaborated to avoid greenhouse gas emissions which negatively impact climate. Hence, in this paper, a finite control set-model predictive current control (FCS-MPCC) method of linear induction machine (LIM) was proposed for linear metro drives to achieve lower thrust ripples and eliminate selection of weighting factor (WF), the main limitation of conventional finite control set-model predictive thrust control (FCS-MPTC). Also, a model reference adaptive system (MRAS) was used for speed estimation due to some environmental considerations and cost-effectiveness. The proposed method used a single cost function that avoided the existence of WF and consisted of primary current errors between the predicted values and their references in αβ-axes. A comparison between the FCS-MPTC and the suggested control method was conducted using Matlab/Simulink under a wide range of operating circumstances and via uncertainty validations issues, on the basis of one 3 kW arc induction machine (which constructed to imitate the actual behavior of the LIM). The extensive simulation results revealed that the proposed FCS-MPCC method can lead to much lower thrust ripples without heavy calculation steps. Moreover, the speed error between the estimated and actual speeds is about 0.025% of the reference value which validates the speed estimation scheme.