Network-Constrained Data-Driven Control of High-Speed Rail Systems: Adaptive and Learning-Based Approaches addresses critical challenges in high-speed railway (HSR) operational control systems, focusing on enhancing safety, efficiency, and automation in an era of rapid network expansion. The book introduces a transformative framework for data-driven adaptive control and multi-train cooperative control under dynamic network constraints. It integrates next-generation 5G-R communication to enable real-time train-to-train (T2T) coordination, reducing dependency on fixed infrastructure and addressing vulnerabilities like faded channels and interference. By combining rigorous theoretical analysis with simulations, the book proposes solutions to improve operational precision, resilience against disruptions, and transportation capacity.

This resource is helpful for researchers, engineers, and graduate students in high speed railway control systems, offering innovative strategies to advance autonomous operations and meet the demands of high-density, high-speed rail networks

• Presents a data-driven adaptive and learning control framework for high-speed trains under network constraints
• Discusses the theory and method of multi-train cooperative control in detail: particularly, how to realize real-time information interaction and dynamic adjustment between trains with the support of train-to-train communication
• Discusses the influence of network constraints (such as fading measurement, malicious attacks, etc.) on train cooperative control, and proposes a series of compensation strategies
• Focuses on current, high-speed rail control technology, but also contains a forward-looking discussion of future high-speed rail communication and control technology, such as the application of 5G-R communication system and autonomous driving technology