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

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 the actual line conditions.From the experimental results,the train operation meets the ATO requirements,the energy consumption is 15.75%more energy-efficient than the actual operation,and the algorithm convergence speed is improved by about 37%.The improved DQN method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation,thereby contributing meaningfully to the advancement of automatic train operation intelligence.

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

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 time-varying,time-delaying and nonlinear control system,especially the braking force changes directly disturb the parking accuracy and comfort.To solve these issues,a predictive control algorithm based on T-S fuzzy model was proposed and applied to the train braking control system.Compared with the traditional PID control algorithm and self-adaptive fuzzy PID control algorithm,the braking capacity of urban rail train was improved by 8%.The algorithm can achieve fast and accurate synchronous braking,thereby overcoming the dynamic influence of the uncertainty,hysteresis and time-varying factors of the controlled object.Finally,the desired control objectives can be achieved,the system will have superior robustness,stability and comfort.

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

The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation.In order to improve the oper-ating energy utilization rate of trains,a multi-objective particle swarm optimiza-tion(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 decision maker,and the satisfac-tory outcomes can be obtained after a limited number of adjustments.The multi-objective particle swarm optimization(MPSO)algorithm is used to optimize the train operation process.An algorithm based on the important relationship between the objective and the preference information of the given reference points is sug-gested to overcome the shortcomings of the existing algorithms.These methods significantly increase the computational complexity and convergence of the algo-rithm.An adaptive fuzzy logic system that can simultaneously utilize experience information andfield data information is proposed to adjust the consequences of off-line optimization in real time,thereby eliminating the influence of uncertainty on train operation.After optimization and adjustment,the whole running time has been increased by 0.5 s,the energy consumption has been reduced by 12%,the parking accuracy has been increased by 8%,and the comprehensive performance has been enhanced.

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

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 of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.

A Markov Model for Subway Composite Energy Prediction

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 train operating conditions for simulation,we found that the proposed control strategy achieves a suitable match between power supply and demand when the train is running.Compared with traditional predictive control systems,energy efficiency 10.5%higher.This system provides good stability and robustness,satisfactory speed tracking performance and control comfort,and significant suppression of disturbances,making it feasible for practical applications.

Adaptive Sliding Mode Control Method for Onboard Supercapacitors System

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 networkbased 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 provide traction energy or absorb regenerative braking energy.In the Boost and Buck modes,the state-space averaging method is used to establish a model and perform exact linearization.An adaptive sliding mode controller is designed,and simulation results show that it can effectively solve the problems of low energy utilization and large voltage fluctuations in urban rail electricity grids,and maximise the recovery and utilization of regenerative braking energy.