Research On Optimization Of ATO Control Strategy For High Speed Train

With the speed of trains continues to increase.Traditional manual driving methods are difficult to meet the performance requirements of trains.Long-term,long-distance,high-density operation can easily cause safety problems such as driver fatigue.Therefore,in order to ensure the safety of high-speed railways to operate efficiently,the Automatic Train Operation(ATO)system will inevitably become an important research direction of train control,and its research also has extremely important practical significance.This thesis firstly analyzes and studies the high-speed train ATO system.The process is as follows: the train’s running process,acceptance conditions and traction braking system are carried out,using the mathematical tool of linear dynamic equations,taking into account the hysteresis of the train’s traction braking system.Based on the characteristics,the train rigid multi-particle model,the train traction braking system model and the ATO system performance index model are established.Since the ATO system has multiple performance indicators,for the multi-objective optimization problem,this thesis establishes a multi-objective optimization model on the premise that the parking accuracy,comfort,punctuality and energy-saving indicators meet the requirements of the ATO system performance indicators,and uses the universal gravitation of particle swarms The Particle Swarm Optimization Gravitational Search Algorithm(PSOGSA)optimizes the target velocity curve.After introducing the dynamic adjustment strategy of the universal gravitational constant,the test function verifies its relatively good convergence and optimization accuracy,and then uses the PSOGSA algorithm to combine multiple The target optimization model calculates and generates the ideal target speed curve,and compares it with the speed curve in the fastest mode.Through comparative analysis,the effectiveness and superiority of the PSOGSA algorithm in optimizing the target speed curve are verified.Aiming at the problem of complex and changeable external interference and uncertain train model parameters during the operation of high-speed trains,this thesis introduces sliding mode control theory,uses the ideal target speed curve generated by the PSOGSA algorithm as the control input of the system,and the train model is designed as the controlled object.The sliding mode controller is optimized by introducing a fractional PID regulator and designing a new variable speed reaching law,and finally design a fractional PID sliding mode controller and applying it to the ATO system to realize the train speed of the ATO system The function of curve tracking control is compared with the PID controller through simulation experiments to verify the control effect and tracking accuracy of the designed controller.From the simulation results,it can be concluded that the fractional-order PID sliding mode controller designed in this thesis has higher tracking accuracy and good control effect are suitable for high-speed train ATO system.