Abstract

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.

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