Application Of Optimal Predictive Control In High Speed Train Automatic Driving

The high speed train automatic driving can reduce the fatigue strength of the driver during long-term high-speed and high-density operation,eliminate the difference caused by different driver levels,and ensure that the train runs automatically and accurately according to the plan.Interval target speed tracking and fixed-point parking are important functions of the Automatic Train Operation(ATO)technology.The interval target speed tracking requires the train to ensure safe and comfortable operation,and the high-speed tracking of the speed curve is carried out.The tracking accuracy has a direct impact on the parking error.If the parking error is too large,the high-speed platform with the screen door will affect the passengers getting on and off,causing many problems such as train delays.Therefore,it is of great significance to research the use of optimal foresight control to achieve target speed curve tracking and fixed-point parking during high-speed train automatic driving operation.Taking the ATO system as the research object,this thesis uses the train operation control theory and method to deeply analyze the train operation process.For the delay delay lag characteristic of the train traction braking system,by linear dynamic equation established mathematical model of the train,and the related theorems are combined,the stability of the train model is verified.Since the train model has a critical pole,the system poles are configured after selecting the appropriate state feedback matrix to stabilize the established train model.Under the constraints of ATO performance indicators such as punctuality rate,comfort,parking accuracy and energy consumption,the Chaotic Particle Swarm Optimization(CPSO)algorithm is used to optimize the train running process,and the ideal target speed curve is generated,and comparative analysis of ATO performance indicators for speed curves before and after optimization to verify the effectiveness of the algorithm.The optimal predictive control is introduced.The optimized speed curve of CPSO algorithm is used as the tracking input of optimal foresight control.The external resistance is regarded as interference.Combined with the advantages of optimal foresight control,the speed controller based on optimal foresight control is designed.The MATLAB simulation is used to compare and analyze the following effects of Optimal Predictive Tracking Controller(OPTC)and PID controller on the target curve.It is verified that the OPTC designed by the optimal foresight control algorithm has better tracking accuracy and can ensure good tracking.In order to suppress the modeling deviation and the external resistance to the system interference,on the basis of the optimal predictive control algorithm,Disturbance Observer(DOB)is added.The interference value observed by the DOB is feedback processed and equivalent to the input to enhance the robustness of the control system.The MATLABsimulation is used to verify the OPTC after DOB addition.Under the condition of external interference and inaccurate modeling parameters,the system has good robustness,which can improve the speed tracking accuracy and reduce the parking error while ensuring passenger comfort.