Velocity-Curve Optimization Based On DE Algorithm And Tracking Control Of Urban Rail Vehicle

With its characteristics of large transportation volume,all-weather and high safety,urban rail transit has become an important way to alleviate traffic congestion and improve the level of traffic safety.However,the running energy consumption of the track train is huge,of which the traction energy consumption accounts for nearly half,so reducing the traction energy consumption of the city rail train has become one of the important problems to be solved in order to maintain the sustainable development of urban rail transit,and it is of practical significance to optimize the running speed curve of the train.The whole city rail system is complex,the influencing factors in the process of train operation are many kinds,and the traditional optimization algorithm can't deal with the optimization issues with a lot of constraints.The evolutionary algorithm is a mature global optimization algorithm with high robustness and wide adaptability,which can effectively solve the complex optimization issues that traditional optimization algorithm can't handle.The main purpose of the ATO system is to simulate the best driver to achieve high-quality automatic operation of the train,to improve the efficiency of the train,passenger comfort and to save energy consumption.For the ATO system,the most important thing is to be able to track the train's optimal speed curve effectively and accurately.By the reasonable and effective tracking control algorithm,this can help the train to achieve good automatic operation.The main work of this thesis is as follows:(1)A multi-mass unit mobile mechanics model is studied in the light of the advantages of the train single-mass point dynamic model and the multi-mass point dynamic model,which can more realistically reflect the train running state and the interaction between the carriers and the structure is simple and easy to analyze.The energy-saving manipulation strategy based on fixed idling search interval is designed,the idling switch point is searched in the fixed interval,which makes the algorithm optimize the search more targeted,considers a number of constraint indicators and establishes the energy-saving optimization model based on the double penalty mechanism of punctuality and comfort.(2)To improve the standard differential evolution algorithm,a collaborative differential evolution algorithm(Referred IMSCDE)based on the strategy of improving variation is proposed to solve the energy-saving optimization model of the train and obtain the optimal running speed curve.The algorithm improves in the selection of population structure,variation strategy and control parameters,and enhances the individual diversity of the population compared with the standard DE algorithm,and improves the global search ability and convergence speed of the algorithm.(3)According to the non-linearity,uncertainty and external interference characteristics of the city rail train system,this paper adopts Robust Adaptive Control theory designs the tracking controller with simple structure,small operation and adaptive ability,and Using Lyapunov theory to prove that the tracking controller designed in this paper is gradual and stable.(4)This thesis uses MATLAB software to simulate the optimization process of the run speed curve optimization and the tracking process of the controller.The simulation results verify the IMSCDE algorithm and the tracking controller are effective in running energy saving and tracking control respectively.