Research On ATO Control Strategy Optimization Of High-speed Train

With the rapid development of high-speed railway in China,higher requirements are put forward for the safety and energy saving of train operation.As an important symbol of railway intelligentization,the automatic train operation(ATO)control system of high-speed train has been paid more and more attention because of its advantages such as ensuring the safe operation of trains,reducing the energy consumption of train operation,improving the efficiency of railway transportation and reducing the driving intensity of drivers.The ATO control system seeks the train operation control strategy to satisfy the optimization target under the given constraint conditions,and it completes the automatic operating function by tracking this strategy through the bottom controller.So it is of great significance to seek a reasonable and efficient ATO control strategy to realize the safe and reliable automatic operation of high-speed train.Aiming at seeking the accurate optimal operation strategy which can meet the requirements of multiple optimization objectives and online optimization,this dissertation makes an in-depth study on the optimization of ATO control strategy of high-speed train.Firstly,on the basis of expounding the basic structure and principle of the ATO control system of high-speed train,the dissertation analyzes the key factors that affect the optimization of the train operation control strategy.The multi-particle model which accurately reflects the force of the train operation process is established,and the coasting of trains passing neutral sections is included into operating conditions.The influence of passing neutral sections on the running speed and running time of the train is solved and analyzed.Secondly,the evaluation indexes of energy consumption,comfort,punctuality,parking accuracy and speeding protection of high speed train are established,and the ATO control strategy is optimized by using krill herd(KH)algorithm.Taking Lanzhou-Xinjiang high-speed railway line as an example data,the sequence of execution distance conversion point in line neutral sections is initialized to individual fixed fragment of krill,and the fitness function of KH algorithm is constructed by using the indexes of energy consumption,comfort of train operation,etc.Compared the particle swarm algorithm and genetic algorithm,simulation results show that the KH algorithm has fast convergence speed and high precision when optimizing the ATO control strategy of high-speed train.The dissertation analyzes the control strategy under the condition of considering the neutral sections and ignoring neutral sections.It also verifies the necessity and rationality of including coasting of trains passing neutral sections into operating conditions when optimizing the ATO control strategy.Finally,considering the problem that most of the existing high-speed train ATO control strategy is optimized offline,the dissertation analyzes the advantages and disadvantages of different control strategies of the remaining operating interval after the original train operation control strategy is affected.And the trigger and termination conditions of the online optimization algorithm are studied.Based on the KH algorithm and introducing the ideas of the simulated annealing algorithm,the position of new krill individuals are selectively accepted to prevent particle deterioration and thus the convergence of the algorithm can be accelerated,and an improved krill herd(IKH)algorithm for optimizing ATO control strategy of high-speed train is proposed.Concurrently,the parallel computation technology is used to improve the optimization efficiency.The online optimization of ATO control strategy of high-speed train is carried out under the condition of satisfying time margin,which demonstrates the effectiveness of the optimization model and optimization algorithm.