This paper addresses the control design for automatic train operation of high-speed trains with protection constraints.A new resilient nonlinear gain-based feedback control approach is proposed,which is capable of gua...This paper addresses the control design for automatic train operation of high-speed trains with protection constraints.A new resilient nonlinear gain-based feedback control approach is proposed,which is capable of guaranteeing,under some proper non-restrictive initial conditions,the protection constraints control raised by the distance-to-go(moving authority)curve and automatic train protection in practice.A new hyperbolic tangent function-based model is presented to mimic the whole operation process of high-speed trains.The proposed feedback control methods are easily implementable and computationally inexpensive because the presence of only two feedback gains guarantee satisfactory tracking performance and closed-loop stability,no adaptations of unknown parameters,function approximation of unknown nonlinearities,and attenuation of external disturbances in the proposed control strategies.Finally,rigorous proofs and comparative simulation results are given to demonstrate the effectiveness of the proposed approaches.展开更多
The existing Big Data of transport flows and railway operations can be mined through advanced statistical analysis and machine learning methods in order to describe and predict well the train speed, punctuality, track...The existing Big Data of transport flows and railway operations can be mined through advanced statistical analysis and machine learning methods in order to describe and predict well the train speed, punctuality, track capacity and energy consumption. The accurate modelling of the real spatial and temporal distribution of line and network transport, traffic and performance stimulates a faster construction and implementation of robust and resilient timetables, as well as the development of efficient decision support tools for real-time rescheduling of train schedules. In combination with advanced train control and safety systems even (semi-.) automatic piloting of trains on main and regional railway lines will become feasible in near future.展开更多
Deep convection systems (DCSs) can rapidly lift water vapor and other pollutants from the lower troposphere to the upper troposphere and lower stratosphere. The main detrainment height determines the level to which th...Deep convection systems (DCSs) can rapidly lift water vapor and other pollutants from the lower troposphere to the upper troposphere and lower stratosphere. The main detrainment height determines the level to which the air parcel is lifted. We analyzed the main detrainment height over the Tibetan Plateau and its southern slope based on the CloudSat Cloud Profiling Radar 2B_GEOPROF dataset and the Aura Microwave Limb Sounder Level 2 cloud ice product onboard the Atrain constellation of Earth-observing satellites. It was found that the DCSs over the Tibetan Plateau and its southern slope have a higher main detrainment height (about 10-16 km) than other regions in the same latitude. The mean main detrainment heights are 12.9 and 13.3 km over the Tibetan Plateau and its southern slope, respectively. The cloud ice water path decreases by 16.8% after excluding the influences of DCSs, and the height with the maximum increase in cloud ice water content is located at 178 hPa (about 13 km). The main detrainment height and outflow horizontal range are higher and larger over the central and eastern Tibetan Plateau, the west of the southern slope, and the southeastern edge of the Tibetan Plateau than that over the northwestern Tibetan Plateau. The main detrainment height and outflow horizontal range are lower and broader at nighttime than during daytime.展开更多
This paper considers the optimal control problem of a single train,which is formulated as an optimal control problem of nonlinear systems with switching controller.The switching sequence and the switching time are dec...This paper considers the optimal control problem of a single train,which is formulated as an optimal control problem of nonlinear systems with switching controller.The switching sequence and the switching time are decision variables to be chosen optimally.Generally speaking,it is very difficult to solve this problem analytically due to its nonlinear nature,the complexity of the controller,and the existence of system state and control input constraints.To obtain the numerical solution,by introducing binary functions for every value of the control input,relaxing the binary functions,and imposing a penalty function on the relaxation,the problem is transformed into a parameter optimization problem,which can be efficiently solved by using any gradient-based numerical approach.Then,the authors propose an adaptive numerical approach to solve this problem.Convergence results indicate that any optimal solution of the parameter optimization problem is also an optimal solution of the original problem.Finally,an optimal control problem of a single train illustrates that the adaptive numerical approach proposed by us is less time-consuming and obtains a better cost function value than the existing approaches.展开更多
Railway transportation plays an important role in modern society.As China's massive railway transportation network continues to grow in total mileage and operation density,the energy consumption of trains becomes ...Railway transportation plays an important role in modern society.As China's massive railway transportation network continues to grow in total mileage and operation density,the energy consumption of trains becomes a serious concern.For any given route,the geographic characteristics are known a priori,but the parameters (e.g.,loading and marshaling)of trains vary from one trip to another.An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption.Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters.However,the problem is difficult to solve due to its high dimension,nonlinearity,complex constraints,and time-varying characteristics.Faced with these difficulties,we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach.Through hierarchical refinement,we learn prediction models of speed and gear.The learned models can be used to derive optimized driving operations under real-time requirements.This study uses random forest and bagging-REPTree as classification algorithm and regression algorithm,respectively.We conduct an extensive study on the potential of bagging,decision trees,random forest,and feature selection to design an effective hierarchical ensemble learning framework.The proposed framework was testified through simulation.The average energy consumption of the proposed method is over 7% lower than that of human drivers.展开更多
基金the National Natural Science Foundation of China(61703033,61790573)Beijing Natural Science Foundation(4192046)+1 种基金Fundamental Research Funds for Central Universities(2018JBZ002)State Key Laboratory of Rail Traffic Control and Safety(RCS2018ZT013),Beijing Jiaotong University.
文摘This paper addresses the control design for automatic train operation of high-speed trains with protection constraints.A new resilient nonlinear gain-based feedback control approach is proposed,which is capable of guaranteeing,under some proper non-restrictive initial conditions,the protection constraints control raised by the distance-to-go(moving authority)curve and automatic train protection in practice.A new hyperbolic tangent function-based model is presented to mimic the whole operation process of high-speed trains.The proposed feedback control methods are easily implementable and computationally inexpensive because the presence of only two feedback gains guarantee satisfactory tracking performance and closed-loop stability,no adaptations of unknown parameters,function approximation of unknown nonlinearities,and attenuation of external disturbances in the proposed control strategies.Finally,rigorous proofs and comparative simulation results are given to demonstrate the effectiveness of the proposed approaches.
文摘The existing Big Data of transport flows and railway operations can be mined through advanced statistical analysis and machine learning methods in order to describe and predict well the train speed, punctuality, track capacity and energy consumption. The accurate modelling of the real spatial and temporal distribution of line and network transport, traffic and performance stimulates a faster construction and implementation of robust and resilient timetables, as well as the development of efficient decision support tools for real-time rescheduling of train schedules. In combination with advanced train control and safety systems even (semi-.) automatic piloting of trains on main and regional railway lines will become feasible in near future.
基金the National Key Research and Development Program on Monitoring, Early Warning and Prevention of Major Natural Disasters (Grant No. 2018YFC1506006)the National Natural Science Foundation of China (Project Nos. 41875108 and 41475037).
文摘Deep convection systems (DCSs) can rapidly lift water vapor and other pollutants from the lower troposphere to the upper troposphere and lower stratosphere. The main detrainment height determines the level to which the air parcel is lifted. We analyzed the main detrainment height over the Tibetan Plateau and its southern slope based on the CloudSat Cloud Profiling Radar 2B_GEOPROF dataset and the Aura Microwave Limb Sounder Level 2 cloud ice product onboard the Atrain constellation of Earth-observing satellites. It was found that the DCSs over the Tibetan Plateau and its southern slope have a higher main detrainment height (about 10-16 km) than other regions in the same latitude. The mean main detrainment heights are 12.9 and 13.3 km over the Tibetan Plateau and its southern slope, respectively. The cloud ice water path decreases by 16.8% after excluding the influences of DCSs, and the height with the maximum increase in cloud ice water content is located at 178 hPa (about 13 km). The main detrainment height and outflow horizontal range are higher and larger over the central and eastern Tibetan Plateau, the west of the southern slope, and the southeastern edge of the Tibetan Plateau than that over the northwestern Tibetan Plateau. The main detrainment height and outflow horizontal range are lower and broader at nighttime than during daytime.
基金the Chinese National Natural Science Foundation under Grant Nos.61563011,61473158,61703012,and 61374006the Ph.D Research Fund of Guizhou Normal University under Grant No.11904–0514170.
文摘This paper considers the optimal control problem of a single train,which is formulated as an optimal control problem of nonlinear systems with switching controller.The switching sequence and the switching time are decision variables to be chosen optimally.Generally speaking,it is very difficult to solve this problem analytically due to its nonlinear nature,the complexity of the controller,and the existence of system state and control input constraints.To obtain the numerical solution,by introducing binary functions for every value of the control input,relaxing the binary functions,and imposing a penalty function on the relaxation,the problem is transformed into a parameter optimization problem,which can be efficiently solved by using any gradient-based numerical approach.Then,the authors propose an adaptive numerical approach to solve this problem.Convergence results indicate that any optimal solution of the parameter optimization problem is also an optimal solution of the original problem.Finally,an optimal control problem of a single train illustrates that the adaptive numerical approach proposed by us is less time-consuming and obtains a better cost function value than the existing approaches.
基金the National Natural Science Foundation of China (Nos.61872217and 61527812)Industrial Internet Innovation&Development Project of Ministry of Industry and Information Technology of China,National Science and Technology Major Project (No.2016ZX01038101)+1 种基金MIIT IT funds (Research and Application of TCN Key Technologies)of Chinathe National Key Technology R&D Program (No.2015BAG14B01-02).
文摘Railway transportation plays an important role in modern society.As China's massive railway transportation network continues to grow in total mileage and operation density,the energy consumption of trains becomes a serious concern.For any given route,the geographic characteristics are known a priori,but the parameters (e.g.,loading and marshaling)of trains vary from one trip to another.An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption.Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters.However,the problem is difficult to solve due to its high dimension,nonlinearity,complex constraints,and time-varying characteristics.Faced with these difficulties,we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach.Through hierarchical refinement,we learn prediction models of speed and gear.The learned models can be used to derive optimized driving operations under real-time requirements.This study uses random forest and bagging-REPTree as classification algorithm and regression algorithm,respectively.We conduct an extensive study on the potential of bagging,decision trees,random forest,and feature selection to design an effective hierarchical ensemble learning framework.The proposed framework was testified through simulation.The average energy consumption of the proposed method is over 7% lower than that of human drivers.