High-speed train control based on multiple-model adaptive control with second-level adaptation

The congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is adopted in our congestion control. The multiple model adaptive control is introduced in this paper based on the weight calculation instead of the parameter estimation in past adaptive control. The model set is composed by the dynamic model based on the fluid flow. And three “local” congestion controllers are nonlinear output feedback controller based on variable RTT, H2output feedback controller, and proportional-integral controller, respectively. Ns-2 simulation results in section 4 indicate that the proposed algorithm restrains the congestion in variable network condition and maintains a high throughput together with a low packet drop ratio.

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Operation-oriented reliability and availability evaluation for onboard high-speed train control system with dynamic Bayesian network

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x18800630 ◽

2018 ◽

Vol 233 (3) ◽

pp. 455-469 ◽

Cited By ~ 1

Author(s):

Lei Jiang ◽

Yiliu Liu ◽

Xiaomin Wang ◽

Mary Ann Lundteigen

Keyword(s):

Control System ◽

Bayesian Network ◽

High Speed ◽

Dynamic Bayesian Network ◽

Dynamic Bayesian Networks ◽

High Speed Train ◽

Train Control ◽

Train Control System ◽

Onboard System ◽

Reliability And Availability

The reliability and availability of the onboard high-speed train control system are important to guarantee operational efficiency and railway safety. Failures occurring in the onboard system may result in serious accidents. In the analysis of the effects of failure, it is significant to consider the operation of an onboard system. This article presents a systemic approach to evaluate the reliability and availability for the onboard system based on dynamic Bayesian network, with taking into account dynamic failure behaviors, imperfect coverage factors, and temporal effects in the operational phase. The case studies are presented and compared for onboard systems with different redundant strategies, that is, the triple modular redundancy, hot spare double dual, and cold spare double dual. Dynamic fault trees of the three kinds of onboard system are constructed and mapped into dynamic Bayesian networks. The forward and backward inferences are conducted not only to evaluate the reliability and availability but also to recognize the vulnerabilities of the onboard systems. A sensitivity analysis is carried out for evaluating the effects of failure rates subject to uncertainties. To improve the reliability and availability, the recovery mechanism should be paid more attention. Finally, the proposed approach is validated with the field data from one railway bureau in China and some industrial impacts are provided.

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