Context of the problem. Artificial Intelligence (AI) has changed the way industries design, build, and enhance control systems. AI uses machine learning and evolutionary algorithms to improve control systems. This technology helps them adapt to critical situations. It also helps them manage noise in input signals and adapt to changing environments. Objectives of the study. Given the large number of scientific papers published in this field, it is necessary to examine and analyze the newest AI techniques applied to control systems, determining recent advances, the advantages of these approaches over traditional methods, and the remaining challenges. Method or Approach to the Study. This paper looks at how AI is used in control systems. It does this by checking existing literature and searching key databases that connect AI and control systems. Main results. The systematic mapping resulted in a detailed review of 184 scientific articles published in the last 15 years. The study showed three key trends: (a) Hybrid control models mix machine learning with traditional methods, (b) Metaheuristic algorithms optimize architectures and parameters, and (c) AI techniques enable adaptive control models. Conclusions or implications. This study highlights current trends and benefits of smart controllers, while also identifying gaps in the literature and proposing future research directions.
Mareco et al. (Fri,) studied this question.
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