Application of Artificial Intelligence in Automated Control Systems

Abstract

The rapid development and widespread application of artificial intelligence technology are profoundly transforming the structural
models and operational logic of automation control systems. In critical fields such as industrial production, intelligent transportation, and
energy dispatching, artificial intelligence endows control systems with capabilities such as data self-perception, model self-learning, and
strategy self-optimization. This breaks through the limitations of traditional control systems that rely on fixed algorithms and predefined rules,
enabling intelligent, autonomous, and highly efficient system operations. Particularly under complex working conditions characterized by
nonlinearity, large disturbances, and uncertainties, artificial intelligence technology can provide robust and adaptive dynamic decision-making
support. As core technologies such as deep learning, reinforcement learning, and expert systems continue to mature, the integration of artificial
intelligence with control systems is advancing toward higher levels of sophistication. This transformation is shifting automation control from
rule-driven models to cognitive decision-making frameworks, demonstrating immense development potential in the context of intelligent
manufacturing and smart industrialization.