Artificial intelligence techniques applied to automatic industrial process control: A systematic review
DOI:
https://doi.org/10.61347/psa.v4i2.175Keywords:
Artificial Intelligence, automatic control, industrial processes, predictive control, PRISMA, reinforcement learning, systematic reviewAbstract
Artificial intelligence (AI) techniques have gained increasing relevance in automatic industrial process control by complementing traditional control schemes and enabling greater adaptation to nonlinear dynamics, prolonged delays, and variable operating conditions. This article aimed to analyze and synthesize the scientific evidence available on the application of artificial intelligence techniques in automatic industrial process control through a systematic review conducted under the PRISMA 2020 methodology. A literature search was performed in databases such as Scopus, Web of Science, IEEE Xplore, ScienceDirect, MDPI, SpringerLink, and De Gruyter, initially identifying 120 records. After the screening process and full-text assessment, 25 open-access articles published in journals with confirmed Scimago quartiles were selected, of which 11 corresponded to Q1 journals and 14 to Q2 journals. The results showed that reinforcement learning had the highest representation among the included studies, followed by model predictive control based on artificial intelligence and intelligent control applied to industrial robotics. Additionally, applications were identified in fuzzy logic, virtual sensors, distributed architectures, and intelligent energy systems. It is concluded that AI offers significant potential to improve the adaptability, robustness, and efficiency of industrial control systems; however, challenges remain regarding model interpretability, validation in real-world environments, and implementation security.
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Copyright (c) 2026 Johana Beatriz Samaniego Palacios, Juan Fernando Pérez Orellana, Wilian Andres Nuñez Yanez, Byron Daniel Erazo Rodríguez
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