Educational contextualizationof predictive control based on artificial neural netwoks in automation training

Abstract

This article aims to propose core pedagogical guidelines for integrating predictive control based on artificial neural networks into university-level automation education. The methodology was based on a systematic review of specialized literature published between 2013 and 2024. The analysis identified technical elements with the greatest educational potential for teaching advanced control. As a result, four core guidelines are systematized. It is concluded that the didactic implementation of predictive neural control requires an intentional approach that transforms mathematical complexity into meaningful learning experiences geared toward developing professional competencies.