Research On The Arrival Curve Optimization And Control Method Of Heavy Haul Train

In recent years,heavy haul train transportation has made remarkable achievements and has been quite remarkable in the development of the world transportation system.With the increase in the national demand for bulk cargo transportation,transportation safety issues have also gradually come to the fore.Especially when the train enters the station automatic control stop,the longitudinal impulse increases during the braking process,resulting in a huge coupler force is easy to cause oupler broken,derailment accidents,there are serious safety risks.The train arrival stop is an unavoidable operation in the process of heavy haul train operation,so it is a key concern and optimization direction to ensure the safety and stability of the train arrival curve.In view of the structural characteristics of heavy haul trains with long body,heavy load capacity and frequent changes in operating conditions,and considering their complex and variable operating routes,randomness and nonlinear dynamics of the environment,we study the optimization of arrival curves and speed tracking control of heavy haul trains based on the mathematical model of coupler buffers and actual operating data.The specific research is as follows:1.Analyze the operating characteristics of heavy haul trains in China,establish a multi particle dynamics model of heavy haul trains,aiming at safety accurately into the station,designed fuzzy adaptive genetic algorithm(FAGA)curves of multi-objective optimization strategy,combining with the real data lines(speed limit,ramps,curve rates,etc.)with the objective function of safety,punctuality and energy saving.Compared with the ordinary genetic algorithm(GA),the results show that the objective function of the arrival curve optimized by FAGA algorithm has greater improvement and better effect.2.Based on the arrival curve optimized by Fuzzy Adaptive Genetic Algorithm(FAGA),Iterative Learning Control(ILC)algorithm is proposed to realize the tracking control of ideal speed displacement curve of heavy haul trains to achieve safe,punctual and energy-saving automatic operation of heavy haul trains.The simulation results show that the iterative learning control algorithm is effective,high precision and small error in the system of heavy haul trains to meet the operation requirements of heavy haul train.3.Considering the limitations of genetic algorithm in the field of multi-objective optimization,and the demand for implementing air braking during heavy train arrival and the practicality of the arrival curve,based on the coupler buffer model of heavy train and actual operation data,design an improved fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)train curve optimization method to optimize the train arrival operation curve with the goal of safety,stability and energy-saving.Secondly,establish a mathematical model of air braking system,design air braking strategy,analyze the delay of air braking force during the braking process,accurately locate the driver’s position for implementing air braking according to the stopping requirements,and optimize the train arrival stopping curve.4.Based on the optimized arrival curves,a high-order model-free iterative learning control algorithm is used to improve the convergence of the controller by applying a high-order form and repeatedly using high-order historical information to learn and continuously correct new inputs.The results are compared with those of the ordinary iterative learning control algorithm in Chapter 2,and the superiority of the proposed method is experimentally verified.