Network-Constrained Data-Driven Control of High-Speed Railway Systems

Adaptive and Learning-Based Approaches

Häftad, Engelska, 2026

1 641 kr

Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt över 249 kr.

Beskrivning

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

Produktinformation

Utgivningsdatum:2026-02-18
Mått:152 x 229 x undefined mm
Vikt:450 g
Format:Häftad
Språk:Engelska
Antal sidor:276
Förlag:Elsevier Science
ISBN:9780443489945

Utforska kategorier

Teknik: allmänt inom Naturvetenskap och teknik

Mer om författaren

Professor Deqing Huang received the B.S. and Ph.D. degrees from Sichuan University, Chengdu, China, in 2002 and 2007, respectively, and the second Ph.D. degree with a major in control engineering from the Department of Electrical and Computer Engineering, National University of Singapore (NUS), Singapore, in 2011. From January 2010 to February 2013, he was a Research Fellow with the Department of Electrical and Computer Engineering, NUS. From March 2013 to January 2016, he was a Research Associate with the Department of Aeronautics, Imperial College London, London, U.K. In January 2016, he joined the Department of Electronic and Information Engineering, Southwest Jiaotong University, Chengdu, China, as a Professor and the Department Head. His current research interests include modern control theory, artificial intelligence, and fault diagnosis as well as robotics Dr Wei Yu received the B.S. degree in rail transportation signal and control from Henan Polytechnic University, Jiaozuo, China, in 2018, the M.S. degree in control science and engineering from the School of Electric Engineering and Automation, Henan Polytechnic University, Jiaozuo, China, in 2021 and the Ph.D. degree in control science and engineering with Southwest Jiaotong University, Chengdu, China, in 2024. He is currently conducting Boya postdoctoral research at Peking University. His research interests include high-speed train control, data-driven control, iterative learning control and networked system control

Innehållsförteckning

1. Introduction2. Preliminaries3. Coordinated MFAC of MHSTs Under Faded Channels and DoS Attacks4. DD Consensus of MHSTs Via Random Topologies with Recovery Mechanism5. Weighted T2T Communication-Based DD Consensus of MHSTs Under DA6. Active Quantizer-Based DMFAC for MHSTs Against Sensor Bias7. HOIM Based Data-Driven ILC of HSTs Subject to Faded Channels8. Fading-Based Coordinated MFAILC of MHSTs Against DoS Attacks9. Attack Recovery-Based DMFAILC for MHSTs with Fading Compensation10. Event-Triggered DMFAILC for MHSTs with Switching Topologies11. DMFAILC for MHSTs under Weighted Communication and Saturations12. DMFAILC for MHSTs Considering Quantizations and Measurement Bias