Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase Drives

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

A Long-Life Predictive Guidance with Homogeneous Competence Promotion for University Teaching Design

Even though planning the educational action to optimize student performance is a very complex task, teachers typically face this challenging issue with no external assistance. Previous experience is, in most cases, the main driving force in curriculum design. This procedure commonly overlooks the students’ perception and weakly integrates the students’ feedback by using a non-systematic approach. Furthermore, transverse competences are, unfortunately, typically omitted in this procedure. This work suggests the use of a predictive tool that determines the optimal application time of different methodological instruments. The suggested method can be used for an infinite number of scenarios of promoted competences. The results can be regarded as a guide to modify the course structure, but, more importantly, it offers valuable information to understand better what is occurring in the teaching-learning process and detect anomalies in the subject and avoid the students’ exclusion. The predictive scheme simultaneously considers the teacher’s perspective, the student’s feedback, and the previous scores in a systematic manner. Therefore, results provide a broader picture of the educational process. The proposal is assessed in a course of Electrical Machines at the University of Malaga during the academic year 2021–2022