Applications of Machine Learning in Real-Time Control Systems: A Review

Abstract Real-time control systems (RTCSs) have become an indispensable part of modern industry, finding widespread applications in fields such as robotics, intelligent manufacturing and transportation. However, these systems face significant challenges, including complex nonlinear dynamics, uncertainties and various constraints. These challenges result in weakened disturbance rejection and reduced adaptability, which make it difficult to meet increasingly stringent performance requirements. In fact, RTCSs generate a large amount of data, which presents an important opportunity to enhance control effectiveness. Machine learning, with its efficiency in extracting valuable information from big data, holds significant potential for applications in RTCSs. Exploring the applications of machine learning in RTCSs is of great importance for guiding scientific research and industrial production. This paper first analyzes the challenges currently faced by RTCSs, elucidating the motivation for integrating machine learning into these systems. Subsequently, it discusses the applications of machine learning in RTCSs from various aspects, including system identification, controller design and optimization, fault diagnosis and tolerance, and perception. The research indicates that data-driven machine learning methods exhibit significant advantages in addressing the multivariable coupling characteristics of complex nonlinear systems, as well as the uncertainties arising from environmental disturbances and faults, thereby effectively enhancing the system’s flexibility and robustness. However, compared to traditional methods, the applications of machine learning also faces issues such as poor model interpretability, high computational requirements leading to insufficient real-time performance, and a strong dependency on high-quality data. This paper discusses these challenges and proposes potential future research directions.