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
1. 1: Introduction - p. 3 2. 2: Preliminaries - p. 31 3. Part Two: Adaptive control approaches - p. P2 4. 3: Coordinated MFAC of MHSTs under faded channels and DoS attacks - p. 39 5. 4: Data-driven consensus of MHSTs via random topologies with recovery mechanism - p. 61 6. 5: Weighted communication-based tracking consensus of MHSTs under deception attacks - p. 83 7. 6: Active quantizer-based distributed MFAC for MHSTs against sensor bias - p. 105 8. Part Three: Learning control approaches - p. P3 9. 7: HOIM based data-driven ILC of HSTs subject to faded channels - p. 125 10. 8: Fading-based coordinated MFAILC of MHSTs against DoS attacks - p. 145 11. 9: Attack recovery-based distributed MFAILC for MHSTs with fading compensation - p. 171 12. 10: Event-triggered distributed MFAILC for MHSTs with switching topologies - p. 193 13. 11: Distributed MFAILC for MHSTs under weighted communication and saturations - p. 215 14. 12: Distributed MFAILC for MHSTs considering quantizations and measurement bias - p. 235 15. Index - p. 255
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
1. 1: Introduction - p. 3 2. 2: Preliminaries - p. 31 3. Part Two: Adaptive control approaches - p. P2 4. 3: Coordinated MFAC of MHSTs under faded channels and DoS attacks - p. 39 5. 4: Data-driven consensus of MHSTs via random topologies with recovery mechanism - p. 61 6. 5: Weighted communication-based tracking consensus of MHSTs under deception attacks - p. 83 7. 6: Active quantizer-based distributed MFAC for MHSTs against sensor bias - p. 105 8. Part Three: Learning control approaches - p. P3 9. 7: HOIM based data-driven ILC of HSTs subject to faded channels - p. 125 10. 8: Fading-based coordinated MFAILC of MHSTs against DoS attacks - p. 145 11. 9: Attack recovery-based distributed MFAILC for MHSTs with fading compensation - p. 171 12. 10: Event-triggered distributed MFAILC for MHSTs with switching topologies - p. 193 13. 11: Distributed MFAILC for MHSTs under weighted communication and saturations - p. 215 14. 12: Distributed MFAILC for MHSTs considering quantizations and measurement bias - p. 235 15. Index - p. 255
