The communication-based train control (CBTC) system ensures the high efficiency and orderliness of trains and is widely used in urban rail transit networks. The adoption of wireless communication and network techniques makes the CBTC systems more vulnerable to cyber attacks. Identity authentication is an effective approach to improve system security. The existing identity authentication mechanisms in CBTC adopt a centralized key management system sensitive to single-point failures. To improve system security, in this article, we deploy a blockchain in CBTC systems. The client that runs the blockchain program not only acts as blockchain nodes to provide distributed key management for the CBTC system but they also work as a relay node to authenticate the communication between train control nodes in CBTC systems. Based on the blockchain-empowered distributed security scheme, the block producer selection and onboard blockchain client handoff decision problem are studied. With the objective to minimize the impact of the key updating process on CBTC system performance and keep the system security under a reasonable level, we formulate the block producer selection and onboard blockchain client handoff decision problem using the deep reinforcement learning approach. Extensive simulation results illustrate that the proposed blockchain-empowered security scheme can significantly improve the CBTC system security, and CBTC systems need to sacrifice part performance to ensure system security.

Communication-based Train Control (CBTC) systems are the burgeoning directions for developing future train control systems. With the adoption of wireless communication and network techniques, train control systems are more vulnerable to cyber-attacks. Notably, the jamming attacks, aiming at the handoff process that is the weakest part of train ground communication systems, will cause long disruption of communication. It will have a severe impact on train control operation efficiency. Current research regarding industry control system security is hard to model the impact of the jamming attacks on the train control system quantitatively, and current countermeasure schemes against jamming attacks are not designed for the operating mechanism of train control systems. This paper first builds the train control security state transition probability model under jamming attacks. A cross-layer defense scheme is then proposed from the aspect of the physical layer, the cyber layer and the management layer. In the physical layer, this paper designs a model prediction control algorithm to track dynamic target signals, in the hopes of eventually tracking the dynamic target quickly and smoothly. In the cyber layer, a multi-stage and zero-sum stochastic game model is built for the channel selection for the attack and the defense, whereby the channel selection randomized policy will be obtained. In the management layer, a dynamic train travel speed profile generation algorithm is proposed to mitigate the jamming attacks’ impact on train control systems. Extensive simulation results are shown that jamming attack impact on CBTC can be mitigated effectively with our proposed cross-layer defense scheme.

Automatic train stop control (ATSC) is a key function of the automatic train operation (ATO) system. An accurate braking process model can help to improve the control strategy. In this paper, the braking process for stop control of high-speed trains is formulated as a single-point time delay model, based on the principle of practical braking processes. Furthermore, a Picard iteration based identification method is first applied to the time delay system, and a train braking process identification method is proposed. The method is straightforward, and the parameters can be identified based on the principle of ordinary differential equations. The effectiveness of the braking process model and the identification method is illustrated by real-life experimental data.

This paper addresses an error-driven nonlinear feedback design technique to improve the dynamic performance of fuzzy adaptive dynamic surface control (DSC) for a class of uncertain multiple-input-multiple-output nonlinear systems with prescribed tracking performance. The highlight of the error-driven nonlinear feedback technique is that the feedback gain self-regulates versus different levels of output and virtual tracking errors, this reflects the classical control design criterions commendably: relatively high feedback gains can be implemented to guarantee disturbances and uncertainties attenuation and so on to improve the control performance when small tracking errors are measured, and relatively small feedback gains can be implemented to circumvent the problems of actuator and states saturations when large tracking errors are measured. The complexity problem of the traditional backstepping design is circumvented owe to the peculiarity of DSC method. Caused by the compound error functions of nonlinear feedback dynamics, a nonquadratic Lyapunov function is used to deduce the conditions of closed-loop stability. Fuzzy logic systems and error transformation-based method are used in the online learning of completely unknown dynamics and the prescribed performance tracking, respectively. Comparative results are presented to demonstrate the effectiveness and preponderance of the proposed control scheme with comparison to existing ones.

Passenger emergency management of urban rail transit station (URTS) has become an indispensable issue with attaching importance to economic benefits and personal security. In this article, a parallel URTS system for passenger emergency management is presented based on artificial systems, computational experiments, and parallel execution (ACP) approach. The agent-based modeling technology is applied to build the artificial URTS system, which contains the models of personal, trains, facilities, events, environments, and center control and decision unit. The computational experiments are performed on the artificial system to analyze and evaluate emergency management strategies. The mechanism of parallel execution between the actual system and artificial system is presented to manage and optimize the emergency strategy, which is capable of guiding the actual URTS system through real-time online supervision and adjustment and providing an active rather than a traditionally passive optimization of passenger emergency management. The ACP-based parallel URTS system provides a novel approach to formulation, evaluation, and optimization of passenger emergency management strategies for URTS.

The state feedback output tracking control problem is investigated for a class of uncertain non-linear strict-feedback system subject to asymmetrically bilateral and time-varying full-state constraints. Non-linear mapping is employed to deal with full-state constraints and the system is transformed into an uncertain pure-feedback system. For the transformed system, an approximation-free backstepping control scheme is proposed by using performance function rather than employing any estimator. It is shown that the proposed control strategy can guarantee that all signals in the closed-loop system are bounded and the tracking errors can be made arbitrarily small by choosing proper control gains. Meanwhile, the states are set within the asymmetrically bilateral and time-varying full-state constraints. At last, two simulation examples are obtained to demonstrate the feasibility and effectiveness of the proposed control algorithm wherein the practical system for metro is considered.

With the rapid development of Metro, it has aroused more attention to its energy efficiency. The regenerative energy is a kind of energy generated by a braking train, which can be used by other trains nearby. Due to the application of regenerative braking and automatic train operation function, more and more studies focus on using regenerative energy by optimal train control. To maximize the utilization of regenerative energy for a couple of trains, we formulate a model and design an algorithm for maximizing the utilization of regenerative energy (MURE) by using the proposed approximate dynamic programming (ADP) approach to adjust the speed curve of the accelerating train. Then, we discuss three approximation methods for the proposed ADP-based approach such as rollout method, interpolation method, and neural network. The rollout algorithm could improve the basic policy for train control with carefully designing. The function approximation method using interpolation could further decrease the energy consumption with assuring punctuality. The neural network approximation usually cannot realize the effect superior to the interpolation strategy due to its complex structure, and it needs more computation time. Finally, the analysis of regenerative energy utilization is given by implementing the numerical experiments with field data from the Yizhuang line, Beijing subway. The numerical results show its effectiveness and stability.

This paper investigates the problem of control with prescribed performance tracking for a class of nonlinear systems in the presence of quantized input. A novel state feedback control scheme by self-scrambling gain is proposed, with the first merit that it is computationally inexpensive, since no linearly-parameterized approximators are used, and the second merit that it is self-adjustable with respect to different levels of tracking errors. Based on a smoothly transformed error variables, the output and virtual tracking errors are guaranteed to converge to some predefined arbitrarily small residual sets regardless of transient and steady bounds. The closed-loop system is proved by rigorously mathematical derivation to be globally stable, and two comparative illustrative examples are given to demonstrate the advantages and effectiveness of the proposed method.

In subway systems, kinetic energy can be converted into electrical one by using regenerative braking systems. If regenerative energy (RE) is fully used, the energy demands from power grid can be dramatically reduced. Since energy storage systems usually have a high cost, they are not considered in this work. Thus, RE has to be immediately utilized by accelerating trains; otherwise, it is wasted into heat via resistors. Timetable optimization methods are often used to coordinate accelerating and braking trains at a station, such that RE can be optimally used by the former. To improve RE utilization (REU) in a subway line, we propose a timetable optimization problem and establish its mathematical model. Many realistic constraints with the decision variables, i.e., headway time and dwell time, are considered. Then we design an improved artificial bee colony (IABC) algorithm to solve the problem. Several numerical experiments are conducted based on the actual data from a subway line in Beijing, China. The correctness of the mathematical model and effectiveness of IABC are shown by comparing it with commercial software CPLEX and a genetic algorithm, respectively. The impact of the decision variables on REU is analyzed, which helps to improve the timetable currently used in this subway line. We also test the robustness of the optimized timetable when certain disturbance takes place.

This paper presents a new nonlinear output and virtual error feedback control technique for stabilization of switched uncertain nonlinear systems using a continuous differentiable nonlinear feedback function (NFF). The merits of the proposed method are threefold: (i) there is no assumption that the switching signals must satisfy the (average) dwell time, (ii) the feedback amplitude self-adjusts under different state levels to guarantee better dynamic performance using the NFF, and (iii) the number of parameters that needs online tuning is 1, i.e., the proposed control method is computationally inexpensive. Moreover, the closed-loop signals are kept bounded using the rigorously proved Lyapunov and Invariant-set theorems, and the output signal converges to a sufficiently small region around zero by choosing proper design parameters. Finally, simulation and comparative results are given to demonstrate the effectiveness of the proposed method.

With the development of new perception, big data, artificial intelligence, and cloud computing techniques, the innovation of railway transportation systems is completely promoted. The parallel rail transportation systems are presented, in which the artificial rail transportation systems are the digitalization, modelling, and software definition of real rail transportation systems. The artificial rail transportation systems are the virtual reconstruction of real rail transportation systems in the cyberspace. The artificial rail transportation systems and real rail transportation systems are coexisting, parallel running, and interacting, which enables data-and-intelligence-driven decisions to lead to a new paradigm of intelligent management, operation, and services of rail transportation systems.

This paper studies the train rescheduling problem on high-speed railway corridor in the situation where contingencies occur and lead to sudden deceleration of some trains. First, we develop an adaptive rescheduling strategy (AR-S) which allows normal trains to use reverse direction track to overtake front decelerating trains based on delay comparison under different path choices. Second, the traditional rescheduling strategy (TR-S) which does not allow any trains to switch tracks is mentioned as a sharp contrast to AR-S. Furthermore, a performance evaluation criterion is designed to evaluate the effectiveness of the train rescheduling approaches. Finally, numerical experiments carried out on Beijing-Tianjin intercity high-speed railway show that AR-S can reduce the total delay of trains up to 24% in comparison with TR-S.

全自动运行(Fully Automatic Operation,FAO)系统是一种基于现代计算机、通信、控制和系统集成等技术实现列车运行全过程自动化的新一代轨道交通控制系统,近年来世界范围内的城市轨道交通系统开始应用全自动运行系统技术.本文在阐述全自动运行系统技术发展和应用现状的基础上,以北京燕房线为例,从FAO系统集成体系、列车无人驾驶技术、综合自动化调度管控技术、基于全生命周期的RAMS综合保障技术等各方面全面介绍了具有完全自主知识产权的中国版全自动运行技术,并对未来该技术的发展方向进行了展望.

Background: Radiotherapy is widely used in patients with osteosarcoma who are not eligible for surgery. However, the therapeutic efficacy of radiotherapy is unsatisfactory. UBE2T is a ubiquitin binding the enzyme E2T which, in turn binds the E3 ubiquitin ligase and induces degradation or functional changes in the substrate. Several recent studies have shown that UBE2T may play a key role as an oncogene in various tumors.Methods: The expression of UBE2T was explored by Gene Expression Omnibus (GEO) database analysis and real-time quantitative PCR (RT-qPCR). UBE2T upregulation in human osteosarcoma was confirmed by immunohistochemical analysis. Small interfering (si) RNA-mediated suppression of UBE2T affected the proliferation and cell cycle of osteosarcoma cells. Downregulation of UBE2T combined with radiation affects the clone formation, migration and apoptosis of osteosarcoma cells. MG-63 cells with the UBE2T knockout were injected into nude mice to induce a xenograft mode.Findings: UBE2T was highly expressed in human osteosarcoma. Suppression of UBE2T inhibited osteosarcoma cell proliferation and induced cell cycle arrest at the G2/M phase. Downregulation of UBE2T combined with radiation may substantially inhibit clonal formation and migration and promote apoptosis of osteosarcoma cells in vitro and vivo.Interpretation: The results indicated that downregulation of UBE2T may enhance osteosarcoma radiosensitivity and serve as a new target for osteosarcoma radiotherapy.Funding: This work was supported Youth Natural Science Foundation of China (No.81700144 ), the Natural Science Foundation of Shandong Province (ZR2014HM093).Declaration of Interest: The authors have declared that no conflict of interest.Ethical Approval: Informed consent was obtained from all patients participating in the present study which was conducted according to the guidelines and with the approval of the Medical Ethics Committee of Jinan Central Hospital.

In this paper, novel adaptive fault-tolerant control (FTC) algorithms are put forward to address the position and velocity tracking control problems of high-speed trains (HSTs). The basic running resistances, additional resistances and interactive forces between the connected carriages are taken into account. On account of the system uncertainties, a novel multidimensional sliding mode surface with time-varying parameters estimated by the adaptive technique is proposed. And neural networks (NNS) are made use of approximating the additional resistances viewed as a bounded disturbance. Two cases for system parameters are taken into consideration: 1) Parameters with unknown boundary are unknown; 2) Parameters with known upper and lower boundary are unknown. As for the former, the unknown parameters are formulated via formal mathematical expression with indicator functions and the adaptive technique is introduced; As for the latter, a continuous functions related to the above multidimensional sliding mode surface is defined and the adaptive technique with Project functions is designed, which guarantees saturation characteristics of the presented controller. The closed-loop system stability can be proved by means of the Lyapunov theory and the feasibility and effectiveness of the presented control schemes can be revealed via simulation experiments.

虚拟重联是一种使列车以编队方式在轨道上追踪运行的技术,从而提高城市轨道交通运营的灵活性和提升线路的通过能力.本文围绕虚拟重联运行仿真和性能衡量,探讨了虚拟重联技术需要的安全制动模型,针对车站瓶颈区域提出了虚拟重联模型和车站追踪改进模型.通过数值计算和计算机仿真建模的方法,对提出的模型进行了仿真验算,结果表明:车站追踪改进模型与相对移动闭塞通过能力相当,虚拟重联模型通过能力最大;系统受到初始延误后,虚拟重联模型的延误恢复能力最强.

In recent years, urban rail transit has been developing rapidly, which provides great convenience for passengers. Consequently more and more wireless hot spots are set up along the track. In our paper, the advantages of wireless positioning in urban rail transit are discussed firstly. Then a fingerprinting train positioning algorithm for metro based on deep learning is proposed. The model of fingerprinting positioning and the simulation environment are established to verify the effectiveness of the algorithm. Finally, the results satisfy the accuracy requirements for train positioning in the research on the energy-efficient operation of metro train and it can be used to provide position information for the energy-saving analysis.

高速铁路运行控制系统是高速铁路的大脑和神经系统,对列车的安全和高效运行至关重要.随着我国高铁里程数和客运量的快速增加,现有的控制手段和调度方法在快速、有效解决列车运行过程中出现的突发事件(比如电力故障、突发地震、山体滑坡、异物侵限等)方面尚有一定差距.目前列车运行控制与调度采用分层架构,突发情况下主要依赖调度员和司机的人工经验进行应急处置,列车晚点时间较长,旅客满意度不高.因此,如何针对高速列车运行过程中可能出现的突发事件,提升其应急处置能力,成为保障高铁安全高效运营的一大难题.本文围绕高铁运行控制与动态调度一体化这一前沿研究热点,对现有运行控制和动态调度的发展现状进行梳理,在此基础上给出一体化的基本架构,明确其基本内涵,最后提出了未来的主要研究方向.

在确保安全的前提下,高速铁路信号系统智能化能够进一步提高运输能力、提升服务水平、降低运营成本,是未来的发展方向.目前,世界各国高速铁路信号系统实现了部分自动化,但大部分工作仍然需要人工操作,亟需研究高速铁路信号系统智能技术,以保持和提升高速铁路技术核心竞争力.高速铁路信号系统的智能技术主要包括智能调度指挥、列车自动驾驶、电务大数据和智能运维等,实现调度指挥智能化、列车控制自动化、运维监控现代化.结合云计算、物联网、大数据、北斗定位、5G通信、人工智能等先进技术,简要阐述高速铁路信号系统智能技术的应用及其技术发展趋势.

The cruise control problems for high-speed trains are investigated in this paper. Both a single train and multiple trains on a railway line are considered. The cars in a single train are modeled as a group of ordered particles connected by flexible couplers. Each car is viewed as an intelligent agent that communicates with its neighbors, making the train a multi-agent system. The information transmission topology among these agents is represented by a connected undirected graph. Distributed cooperative control laws are constructed that achieve displacement and speed consensus among cars at a desired profile, while guaranteeing the coupler displacements to be within a safety range and converge to the nominal value. For multiple trains on a railway line, each train has access to the information of the trains within its wireless communication range, making all cars in these trains a multi-agent system. The underlying communication topology is now a state-dependent undirected graph. Distributed control laws are designed such that, besides achieving coordinated control of cars among each train, consensus among trains at the desired displacement and speed profile and connectivity among trains are also achieved, while avoiding collision. Extensive simulation results are presented to illustrate the theoretical conclusions we have reached.