Abstract
<jats:p>Currently, manufacturing and artificial intelligence are fundamental for organizations, providing optimal decision-making, accurate maintenance forecasts, and reduced manufacturing equipment costs, resulting in higher-quality production. The implementation of new technologies, such as neural networks applied to motors in transient states, expands the possibilities for achieving continuous improvement in company productivity. This research proposes the use of neural networks to control the speed and position of a DC motor as part of a continuous improvement in efficiency levels and real-time monitoring, allowing for data analysis and simulation of automatic control systems. The methodology used is non-experimental and cross-sectional, simulating a control design interacting with an neural network in the MATLAB programming environment and with a LabVIEW module interacting with a Raspberry Pi computer, ideal for data acquisition and control.</jats:p>