Xubin Sun

Regenerative Braking Energy Utilization by Multi Train Cooperation

Regenerative braking system is widely used on subway trains, which will transmit kinetic energy of the trains to electricity. When the braking speed of a train is comparatively high, regenerative braking is prior to the mechanical braking. However, if the regenerative braking energy can not be absorbed by other trains in the same power supply section, the regenerative braking energy may lead to the voltage rising, even have to use dissipative resistance to absorb the surplus energy. The expected situation is that the regenerative braking energy is absorbed by other trains in the same power supply section as much as possible. Multi-train cooperation method is given in this paper, where the speed profile of the trains, selected to absorb the regenerative braking energy, will be partly adjusted. Typically, part of the original speed profile will be replaced by coast-accelerate-coast strategy, the objective is to make the train run as far as possible by only using the distributed regenerative energy. A case is studied based on Beijing Yizhuang Subway line, where speed profiles of two trains are adjusted to absorb the regenerative braking energy generated by a braking train at the same power supply section.

Robust Consensus of Nonlinear Multiagent Systems With Switching Topology and Bounded Noises

Consensus of multiagent systems (MASs) is an intriguing topic in recent years due to its widely used application in robotics, biology, computer, and social science. In the real world, the evolution of MAS is inevitably involved in dynamical environments and the recent development of MAS calls for novel tools for the analysis of MAS with dynamic topology. In addition, the interactions between agents are generally nonlinear and environmental noises are ubiquitous in the communication channels between agents. However, the existing investigation on MAS places little attention on nonlinear models and the inner relationship between external disturbance and consensus is still unclear. Facing these problems, this paper considers an MAS in which the interactions between agents are nonlinear and the communication between agents are infected by environmental noises. By using a novel method of nonsmooth Lyapunov candidate, it has been demonstrated that such an MAS can realize robust consensus under the conditions of jointly (sequentially) connected topology and bounded noises. Finally, simulation results validate the effectiveness of these criteria.

A Super-Twisting-Like Algorithm and Its Application to Train Operation Control With Optimal Utilization of Adhesion Force

The friction between wheel and track is usually called adhesion force, and it is the critical factor for the movement of trains. On one hand, excessive driving force of a train may lead to insufficient utilization of the adhesion effect and cause wasted energy; on the other hand, insufficient driving force of a train brings inefficient train operation. To balance the issues of energy consumption, operational efficiency, and security, it is necessary to control a train to obtain its maximal adhesion force, particularly in the cases of fast acceleration and emergency braking. However, since engineering experiments indicate a complex nonlinear relationship between the adhesion force and the slip ratio of a train, such a control problem is difficult and challenging, particularly when the optimal slip ratio is unknown. Facing this problem, this paper proposes a novel control method based on the modification of the famous super-twisting sliding mode algorithm, and rigorous mathematical analysis is given to guarantee the ultimate boundedness of the proposed algorithm. Furthermore, by considering four different control scenarios, detailed control and estimation algorithms are both proposed. Simulation result verifies that the proposed control strategy can control the train to obtain its maximum adhesion force.

Emergency Management of Urban Rail Transportation Based on Parallel Systems

Integrating artificial systems, computational experiments, and parallel execution (ACP) is an effective approach to modeling, simulating, and intervening real complex systems. Emergency response is an important issue in the operation of urban rail transport systems for ensuring the safety of people and property. Inspired by the ACP method, this paper introduces a basic framework of parallel control and management (PCM) for emergency response of urban rail transportation systems. The proposed framework is elaborated from three interdependent aspects: Points, Lines, and Networks. Points represent the modeling of urban rail stations, Lines describe the microscopic characteristics of urban rail connections between designated stations, and Networks present the macroscopic properties of all the urban rail connections. Based on the given framework, a series of parallel experiments, which were impossible to achieve in real systems, can now be conducted in the constructed artificial system. Furthermore, the constructed artificial system can be used to test and develop effective emergency control and management strategies for real rail transport systems. Therefore, this proposed framework will be able to enhance the reliability, security, robustness, and maneuverability of urban rail transport systems in case of an emergency.

Adaptive fault-tolerant automatic train operation using RBF neural networks

In order to accommodate actuator failures which are unknown in amplitude and time, adaptive fault-tolerant control schemes are proposed for automatic train operation system. Firstly a basic design scheme on the basis of direct adaptive control is considered. It is demonstrated that, when actuator failures occur, asymptotical speed and position tracking are guaranteed. Then a new user-friendly control scheme is proposed which can eliminate the undesirable chattering phenomenon, which is the defect of the previous method. Simulation results verify the effectiveness of established theoretical results that satisfactory speed tracking and position tracking can be guaranteed in the presence of uncertain actuator failures in automatic train operation systems.

Modeling of Crowd Evacuation With Assailants via a Fuzzy Logic Approach

Modeling and analyzing the behaviors and characteristics of crowds in emergency is a challenging task with significant practical meanings. In this paper, a fuzzy logic approach is proposed to describe crowd evacuation behaviors, taking into account the effect of assailants. First, the microscopic pedestrian model and the assailant model are developed according to their different intentions in evacuation scenarios. Pedestrians are further divided into three categories depending upon whether they are affected by assailants. The individuals behaviors are determined by the integration of recommendations of local obstacle-avoiding behavior, regional path-searching behavior, and global goal-seeking behavior with adjustable weighting factors, which are automatically adjusted based on the perceptual information obtained from the complex interaction with surrounding environments. Then, the proposed pedestrian model is validated by comparing the simulated fundamental diagram with a large variety of empirical and experimental data. Finally, simulations in a hall with a single exit are implemented. It is shown that the model can truly reappear typical collective phenomena such as “arching and clogging” and “faster-is-slower effect.” The variations of the model and scenario parameters, such as pedestrians desired speed, exit width, assailants desired speed, and duration of attack, greatly influence the evacuation efficiency. In addition, a novel “circuity phenomenon,” i.e., pedestrians will give up the direction of goal when they encounter assailants or they see assailants and, at the same time, perceive a very crowded exit, is observed in crowd evacuation simulations.

Optimal train schedule with headway and passenger flow dynamic models

This paper proposes an optimization method of train schedule for metro line, which will make the timetable, including train dwell time, in line with passenger demand. This method is also provide space for timetable improvement in robustness and energy-saving. Train dwell time is modeled to calculate an optimal dwell time at each station for boarding and alighting passengers, except headway equation and passenger equation. Train operation schedule includes the train dwell time at stations, running time between adjacent stations and headway, where dwell time at stations determines the process of passenger exchanging between stations and trains. A train schedule model is established with constrains of headway equations, passenger equations, and train dwell time equations, where the train dwell time is modeled as a function of the passenger boarding and alighting volumes. The aim of the optimal problem is to minimize the waiting time of passengers and operation cost directly or indirectly. Lagrangian duality theory is adopted to solve this optimal problem. Simulation results illustrate that this method is efficient to generate the train schedule, which meets the passengers exchanging requirements between trains and platforms.

Analysis and design of Driver Advisory System (DAS) for energy-efficient train operation with real-time information

Energy efficiency has become one of the major concerns in railway operation today. Generation of energy-efficient train trajectories can be modeled as solving a complex optimization problem with nonlinear and time-varying variables under multiple constraints of equality and inequality, as train operation is a dynamically vacillating process due to complex railway operational conditions. In recent years Driver Advisory System (DAS) is introduced as the system to deliver optimized energy-efficient trajectory to train operation through advisory to the driver based on static or real-time railway operation information. This work proposes a prototype DAS design using a PC as central unit and a smartphone as on-board unit, with a bi-directional message exchange between central unit and on-board unit. An energy-distribution based model and optimization algorithm is developed to derive and to adjust train trajectories. Simulation results are demonstrated the efficiency and robustness of the approach and methods.

Train operation adjustment of the urban rail transit based on dwell time model

Train operation adjustment (TOA) plays an important role in ensuring transportation order and enhancing transportation efficiency, the purpose of TOA is to make the error between actual schedule and planned schedule as small as possible, According to the influence of passenger flow on train delay, this paper builds a novel multi-objective train adjustment model by incorporating the dwelling time model of trains in each station. In order to resolve the proposed TOA problem, we introduce the mutation gene and the hybrid gene to improve the PSO algorithm. Finally, simulation results verify the validity of the introduced model and the better performance of the improved algorithm with respect to the normal PSO algorithm.

Particle-optimized control for automatic train operation based on sliding mode observer

This paper investigates the automatic control problem of high speed train systems under uncertain resistance conditions such as time-varying resistance, unknown aerodynamic drag and wind gust. A sliding mode observer (SMO) based control method is designed for tracking given position-velocity profile precisely. The control method is disturbance rejective that does not rely on specific resistance coefficient. Proposed Methods parameters have been optimized by particle swarm optimization. The effectiveness of the proposed method is verified via numerical simulations.