Data-Driven Event-Triggered Cooperative Control for Multiple Subway Trains With Switching Topologies

In this paper, a data-driven event-triggered cooperative control (DD-ETCC) scheme is proposed for the multiple subway trains to realize speed tracking and dynamic headway adjustment under switching topologies. Firstly, a nonlinear subway train system is transformed into a linearization data model, a...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 23; no. 9; pp. 14702 - 14711
Main Authors: Wang, Qian, Jin, Shangtai, Hou, Zhongsheng
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aerodynamics
Algorithms
Control systems
Cooperative control
Data models
Data-driven cooperative control
event-triggered control
Linearization
multiple subway trains
Public transportation
Resistance
Switches
Switching
switching topologies
Topology
Tracking errors
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:In this paper, a data-driven event-triggered cooperative control (DD-ETCC) scheme is proposed for the multiple subway trains to realize speed tracking and dynamic headway adjustment under switching topologies. Firstly, a nonlinear subway train system is transformed into a linearization data model, and the complex nonlinear terms caused by mechanical and aerodynamic resistance and operating environment conditions are estimated by projection algorithm. Then, the DD-ETCC scheme based on the linearization data model and the asynchronous event-triggered condition for multiple subway trains with switching topologies is designed. Theoretical analysis shows that the speed tracking errors of multiple subway trains are bounded, and the headway distances of consecutive trains are stabilized in a safe range with the proposed scheme. Finally, the effectiveness of the proposed DD-ETCC scheme of multiple trains is illustrated by subway train simulations.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3131997