A diverse group of so-called multi-vector techniques has recently appeared to enhance the control performance of multiphase drives when a direct control strategy is implemented. With different numbers of switching states and approaches for estimating the application times, each multi-vector solution has its own nature and merits. Previous studies have individually tested each version of the proposed finite-control-set model predictive control (FCS-MPC) strategies using a single experimental setup with specific parameters and, in some cases, using a limited range of operating conditions and focusing exclusively on some control aspects. Although such works provide partial contributions, the control performance is highly affected by the test and rig conditions, being dependent on the machine parameters, the switching frequency and the range of operation. Consequently, it becomes difficult to extract some universal conclusions that guide the control designer on the best alternative for each application. Aiming to enrich the knowledge in this field and provide a broader picture, this work performs a global analysis with different multi-vector techniques, various machine parameters, multiple operating points and a complete set of indices. Experimental results confirm that the selection of the most adequate control strategy is not a trivial task because the degree to which multi-vector techniques are affected by the test conditions is variable and complex. Some tables with a qualitative analysis, based on the extensive empirical tests, contribute with a more complete insight and guide eventual control designers on the decision about the optimal regulation approach to be chosen.

Model predictive control (MPC) is an interesting regulation solution in the field of multiphase electric drives. Its performance has been improved from different perspectives, but most works implement a single-step horizon approach. The complexity of using long-prediction horizons along with the higher complexity of multiphase systems has hindered the real-time implementation of multistep strategies. In fact, the only attempt of designing a multistep solution for multiphase drives uses a smart selection of the voltage vector, reducing the computational burden. Unfortunately, this strategy completely disregards the voltage output selected as the better candidate in the second step (i.e., in $\boldsymbol{k+3}$ ), hence missing valuable information that can be inherited from one sampling period to the next one. This work suggests informing the MPC by creating a so-called hall of fame that stores the switching state determined as a suitable candidate in the previous sampling time. Such vector is promoted in the iterative process by adding a term in the cost function that is activated when the switching state under evaluation is within the hall of fame. Experimental results in a six-phase drive confirm the capability of the proposed scheme to simultaneously improve the current tracking and reduce the switching frequency.

Both industry and academia have recently highlighted the need to provide students with 21st-century abilities that involve soft skills and flexible technical competencies. To this end, active learning (AL) helps scholars to be more autonomous, proactive, self-regulated, sociable and creative. A full AL implementation typically requires costly resources and institutional support. However, it is possible to maintain a traditional teaching structure and include low-cost AL activities that enhance the students’ performance and satisfaction. This proposal suggests a methodology where lectures become more dynamic, the practical teaching is student-centered, the lab teaching is fully open, and the learner’s progress is gamified. A full subject design is presented in a comprehensive manner aiming for satisfying courses with a heterogeneous students’ profile. All activities are carefully selected with a social constructivist approach in order to promote critical thinking, social interaction and students’ engagement. The assessment of the course design implementation is performed considering multiple aspects, such as satisfaction, performance, social relationships and self-regulation. Results confirm the adequacy of the proposal to develop technical skills, excite non-technical competences and motivate scholars with an inclusive approach.

Model predictive control (MPC) is a popular control technique to regulate multiphase electric drives (EDs). Despite the well-known advantages of MPC, it is sensitive to parameter detuning and lacks the capability to eliminate steady-state errors. The appearance of an offset between the reference and measured currents can significantly jeopardize the performance of the ED. This article suggests the use of a memory-based model predictive control (MB-MPC) that activates a compensation term when the parameter mismatch is detected. The suggested MB-MPC is universal for any multiphase machine if spatial harmonics are neglected since the proposed method does not consider any of the secondary x–y planes. Experimental results in two different rigs with six- and nine-phase induction motors prove this universality as well as its capability to eliminate current and speed offsets.

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.

Speed variable electric drives play a key role in the evolution of electrical mobility. The dynamic performance of these systems is a crucial feature for security purposes. For this reason, a large number of works are focused on identification of the most appropriate control technique to satisfy a transient scenario. In this regard, the dynamic abilities of linear and direct controllers were analysed for three-phase drives. Although some insights about their transient performance were obtained, there are yet some issues to be solved. For instance, speed response was typically omitted, some influencing factors were neglected or the multiphase case was carried out. Considering this information, this work proposes a comparative analysis of the dynamic performance of the most popular regulation strategies for a six-phase electric drive. This study analyses speed, current and voltage responses to achieve an overall view of the system performance. Two concepts were employed to simplify the comprehension of the dynamic behavior of a regulation strategy: reaction time and response capacity. Experimental results are employed to confirm the impact of the different agents on a transient situation.