Thanks to the researchand development efforts, multiphase electric drives can be considered a reality for industrial applications, especially when high-performance requirements are mandatory. However, there are still various issues to address in order to fully exploit all the multiphase advantages. For example, the usage of high sampling frequencies can modify the control action effect and, consequently, the selection criterion of direct regulation schemes can become questionable. Focusing on this fact, this work proposes a data analysis to identify undesirable switching patterns of model predictive control (MPC) when the control period is reduced. Based on the conclusions drawn from the mentioned analysis, a new cost function is designed to avoid the application of these suboptimal voltage sequences. This control solution simultaneously enhances signal quality and reduces switching losses. A series of experimental results using different six-phase machines has been obtained to confirm the effectiveness of the proposed control scheme.

Multiphase electric drives (EDs) offer important advantages for high-demand applications. However, they require appropriate high-performance control strategies. In this context, finite-control-set model predictive control (FCS-MPC) emerges as a promising strategy, offering a notable flexibility to implement multiobjective regulation schemes. When applied to multiphase EDs, standard FCS-MPC exhibits degraded current quality at low and medium control frequencies. Multivector solutions address this issue by properly combining multiple voltage vectors within a single control period to create the so-called virtual voltage vectors (VVVs). In this way, this approach achieves flux and torque regulation while minimizing current injection into the secondary subspace. For this purpose, the VVV synthesis typically prioritizes active vectors with low contribution in secondary subspaces, avoiding the average deception phenomenon. VVV solutions commonly enable an open-loop regulation of secondary currents. Nevertheless, the absence of closed-loop control in the secondary subspace hinders the compensation of nonlinearities, machine asymmetries, and unbalanced conditions in the ED. Considering this scenario, this work implements a multivector FCS-MPC recovering closed-loop control for the secondary subspace. The capability of the proposal to mitigate secondary current injection and compensate for possible dissymmetries is experimentally evaluated in a six-phase ED. Its performance is compared against a benchmark technique in which secondary current regulation is handled in open-loop mode. The proposed control solution significantly improves in current quality, achieving a reduction in harmonic distortion of 54% at medium speed.

Multiphase electric drives have gained significant attention in recent years due to their enhanced efficiency and inherent fault-tolerant capability, making them a promising solution for modern high-performance applications. In this context, finite control set model predictive control (FCS-MPC) has emerged as an effective control strategy due to its flexibility in handling multivariable systems and multiple control objectives. Among its recent developments, variable-sampling-time approaches introduce an additional degree of freedom that enables more efficient adaptation of the control action, particularly reducing switching frequency. This variant of FCS-MPC schemes is based on a sequential structure, in which the direction of the desired current response is prioritized over its magnitude, even when implementation constraints limit its achievement. This work proposes an adaptive sampling time multivector model predictive control strategy (AST-MPC) for six-phase induction motor (6ph-IM) drives. The proposed AST-MPC combines multivector control actions with a threshold-based mechanism to incorporate magnitude information into the selection of control actions, typically governed by directional criteria. The designed approach is experimentally validated and compared under steady-state and transient conditions using multiple performance metrics. Results demonstrate that AST-MPC achieves improved current quality and reduced switching frequency, maintaining suitable dynamic performance and providing natural fault tolerance.

Finite-control-set model predictive control (FCS-MPC) schemes based on virtual voltage vectors (VVVs) have emerged as a promising solution to mitigate harmonic distortion in multiphase motors. Numerous studies have demonstrated the effectiveness of this approach at lowto medium control frequencies. However, nonlinearities in voltage source converters (VSCs), such as dead time, can degrade the performance when short control periods are employed. This issue has been typically solved in linear regulation schemes by modifying reference voltages or adding specific controllers. Unfortunately, these approaches are not applicable for direct strategies, such as FCS-MPCs. To address this situation, this work proposes an FCS-MPC strategy capable of mitigating the dead-time effect when VVVs are applied. Specifically, the duty cycle of each multivector control action is re-estimated considering VSC nonlinearities. Nevertheless, this first stage cannot prevent the generation of current harmonics resulting from dead time during transitions between control periods. For this reason, in a second stage, the cost function is further informed to account for the presence of low-order harmonics caused by the VSC dead time, enabling the online suppression of these undesired currents. Extensive simulation and experimental results on a six-phase drive validate the effectiveness of the proposed scheme in mitigating distortions induced by VSC nonlinearities at high frequencies.