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  • Open Access


    Automatic Driving Operation Strategy of Urban Rail Train Based on Improved DQN Algorithm

    Tian Lu, Bohong Liu*

    Journal on Artificial Intelligence, Vol.5, pp. 113-129, 2023, DOI:10.32604/jai.2023.043970

    Abstract To realize a better automatic train driving operation control strategy for urban rail trains, an automatic train driving method with improved DQN algorithm (classical deep reinforcement learning algorithm) is proposed as a research object. Firstly, the train control model is established by considering the train operation requirements. Secondly, the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem. Finally, the priority experience playback and “restricted speed arrival time” are used to reduce the useless experience utilization. The experiments are carried out to verify the train operation strategy method by simulating More >

  • Open Access


    Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

    Liang Jin1,*, Qinghui Meng1, Shuang Liang2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 387-395, 2022, DOI:10.32604/csse.2022.017745

    Abstract The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation. In order to improve the operating energy utilization rate of trains, a multi-objective particle swarm optimization (MPSO) algorithm with energy consumption, punctuality and parking accuracy as the objective and safety as the constraint is built. To accelerate its the convergence process, the train operation progression is divided into several modes according to the train speed-distance curve. A human-computer interactive particle swarm optimization algorithm is proposed, which presents the optimized results after a certain number of iterations to the… More >

  • Open Access


    Model of a Composite Energy Storage System for Urban Rail Trains

    Liang Jin1,*, Qinghui Meng1, Shuang Liang2

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 1145-1152, 2022, DOI:10.32604/csse.2022.017744

    Abstract This article has no abstract. More >

  • Open Access


    Adaptive Sliding Mode Control Method for Onboard Supercapacitors System

    Yanzan Han1,*, Hang Zhou1, Zengfang Shi1, Shuang Liang2

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 1099-1108, 2022, DOI:10.32604/csse.2022.017741

    Abstract Urban rail trains have undergone rapid development in recent years due to their punctuality, high capacity and energy efficiency. Urban trains require frequent start/stop operations and are, therefore, prone to high energy losses. As trains have high inertia, the energy that can be recovered from braking comes in short bursts of high power. To effectively recover such braking energy, an onboard supercapacitor system based on a radial basis function neural network-based sliding mode control system is proposed, which provides robust adaptive performance. The supercapacitor energy storage system is connected to a bidirectional DC/DC converter to More >

  • Open Access


    A Markov Model for Subway Composite Energy Prediction

    Xiaokan Wang1,2,*, Qiong Wang1, Liang Shuang3, Chao Chen4

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 237-250, 2021, DOI:10.32604/csse.2021.015945

    Abstract Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard More >

  • Open Access


    Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

    Xiaokan Wang*, Qiong Wang

    Journal on Internet of Things, Vol.3, No.1, pp. 1-9, 2021, DOI:10.32604/jiot.2021.010228

    Abstract A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve. In the train control system, the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme, and the initial population can be formed by the way. The fitness function can be designed by the design requirements of the train control stop error, time error and energy consumption. the effectiveness of new individual can be ensured by checking the validity More >

  • Open Access


    Predictive Control Algorithm for Urban Rail Train Brake Control System Based on T-S Fuzzy Model

    Xiaokan Wang1, 2, *, Qiong Wang2, Shuang Liang3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1859-1867, 2020, DOI:10.32604/cmc.2020.011032

    Abstract Urban rail transit has the advantages of large traffic capacity, high punctuality and zero congestion, and it plays an increasingly important role in modern urban life. Braking system is an important system of urban rail train, which directly affects the performance and safety of train operation and impacts passenger comfort. The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity. Also, urban rail transit has the characteristics of high speed, short station distance, frequent starting, and frequent braking. This makes the braking control system constitute a… More >

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