Model predictive current control (MPCC) has recently become a viable alternative for multiphase electric drives, because it easily exploits the inherent advantages of multi-phase machines. However, the prediction in MPCC requires a high number of voltage vectors (VVs), being therefore computationally demanding. In that regard, this paper proposes a computationally efficient MPCC of an asymmetrical six-phase induction machine drive (ASIMD) that reduces the number of VVs used for prediction. By using the characteristics of the deadbeat control (DB), the proposed method obtains a reference voltage vector (RVV), where its position will serve as a reference and integrates the MPCC scheme. Only 4 out of 13 predictions are needed to determine the best VV, dramatically reducing the algorithm computation. Experimental results for a six-phase case study compare the standard MPCC with the suggested method, confirming that deadbeat model predictive current control (DB-MPCC) shows that the execution time can be shortened by 48.8% and successfully improve the motor performance and efficiency.

Multiphase drives have been presented as potential replacements for conventional three-phase machines, primarily because of their propensity to operate faultlessly. Due to the various stator phase arrangements, standard fault detection techniques are insufficiently applicable and cannot be used to diagnose faults in the various configurations of multiphase machines in closed-loop applications. The current study proposes an effective online diagnostic technique based on the computing and tracking of a significant severity factor, which is defined as the ratio of the zero, negative, and positive voltage symmetrical components employing a short-time least-square Prony algorithm (STLSP). In this study, the asymmetrical six-phase induction motor (ASPIM) was controlled by a model predictive control (MPC) algorithm, an attractive control scheme for the regulation of multiphase electric drives, since it easily exploits their inherent advantages. This article addresses stator faults in ASPIMs. The effectiveness of the suggested strategy was confirmed experimentally for various operating conditions in both steady and transient states.

The conventional model predictive control (MPC) is an attractive control scheme for the regulation of multiphase electric drives, since it easily exploits their inherent advantages. However, as the number of phases increases, the MPC’s complexity increases exponentially, posing a high computational burden. Additionally, the MPC still presents other issues related to the weighting factor design in the cost function. Accordingly, this paper proposes a low-complexity hysteresis model predictive current control (HMPCC) that can significantly reduce the computational burden, improve the motor’s performance, and completely avoid the weighting factor design. The proposed method is a hybrid control method, consisting of two distinct controls that complement one another. The hysteresis control is used to reduce the number of iterations per sampling period, thereby reducing the computational effort required to choose the voltage vector that actively produces torque/flux, and nullifying the weighting factor requirement. Finally, the MPC is used to improve the torque and current quality. The effectiveness of the proposed method is verified through experimental data, and the results emphasize the improvement of the proposed HMPCC scheme.

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