Abstract
An analytical review of scientific studies in the field of quality assurance for hotrolled steel products is presented, with a focus on adaptive control systems. The main factors affecting the stability of the rolling process are considered, including deviations in strip thickness and profile, temperature conditions, and dynamic loads. Special attention is given to the role of modern adaptive algorithms that can promptly adjust rolling parameters in real time. The literature review highlights the advantages of adaptive thickness control based on neural network and fuzzy logic regulators. Approaches to adaptive control of interstand tension and the use of loopers to stabilize the process are analyzed. A key aspect is the use of digital observers that monitor parameters not directly measurable, such as elastic torque in the rolls. The relevance of intelligent algorithms for real-time detection of shape defects and surface damage is emphasized. The analysis confirms that the use of adaptive control systems significantly improves the quality of hot-rolled products and reduces the risk of defects during the rolling process.