A Cognitive Structure Based Stop Control Algorithm Of High-speed Train

High-speed train station stop is an important function of the automatic train operation(ATO)system,this function needs to realize accurate stop control while ensuring passenger comfort.Aiming at the problem of high-speed train accurate stop control,this thesis focuses on analyzing the cognitive processing process of the human high-speed train driver during train stop control process,and proposes a stop control algorithm based on cognitive structure.The main work and research results of this thesis are as follows:(1)The cognitive process model of the high-speed train driver was constructed.The high-speed train driver’s cognitive task analysis was carried out,and the cognitive task characteristics of its information perception,thinking processing and output process were obtained.On this basis,the cognitive process was analyzed,and a high-speed train stop cognitive structure model was constructed,which included the high-speed train driver’s perception,memory processing,thinking decision and output process model during the train stop control process.(2)The cognitive stop control algorithm for the high-speed train was designed.Based on the above cognitive process model,the knowledge chunks and skill productions of each cognitive process model were defined,and the cognitive and task parameters of the algorithm were set by analyzing the memory retrieval and learning mechanism of the cognitive structure,thereby achieving the high-speed train cognitive stop control algorithm.(3)A high-speed train stop control algorithm simulation test platform was designed and implemented.This thesis analyzed the design requirements of the simulation test platform,designed its overall structure,and completed the software design of the simulation test platform.The simulation test platform finally realized can simulate the operating characteristics of the CR400 AF high-speed train and visualize the operation process of the train,and can evaluate and analyze the high-speed train stop control algorithm.At the end of the thesis,a simulation analysis of the proposed high-speed train cognitive stop control algorithm was carried out,through the comparison with PID control algorithm,the advantages of the algorithm proposed in this thesis in driving performance and cognitive performance were analyzed.The results showed that the algorithm in this thesis was more excellent in performance indicators such as the number of adjustments,impact rate,and had good adaptability to different initial conditions.The algorithm had similar driving characteristics to human high-speed train drivers,and can effectively achieve human-like train stop control and driving effects.