Research On Speed Tracking Control Of Permanent Magnet Maglev Train Based On Predictive Fuzzy PID Control Algorithm

In recent years,China's rail transit has developed rapidly.As a new type of rail transit,the suspended rare earth permanent magnet maglev train came into being.It is especially suitable for public transportation and urban landscape browsing transportation in small and mediumsized cities with beautiful scenery and undulating terrain.It is the first choice for the transportation system of "characteristic small towns".However,the suspended permanent magnet maglev train is always suspended,its structure is more complex,the control system has a large time delay,many constraints,and a complex operating environment,so it is easy to be affected by various disturbances during driving.To solve the above problems,this paper proposes an improved genetic algorithm to obtain the train speed curve with better performance indexes and designs a weighted predictive fuzzy PID(WM-F-PID)control algorithm for train speed curve tracking.The specific work contents are as follows:Firstly,the speed optimization and tracking control model of the suspended permanent magnet maglev train is established.This part mainly analyzes the force of the suspended permanent magnet maglev train in different operation states,and establishes the train dynamics model;At the same time,taking multiple performance indexes such as train energy consumption,comfort,punctuality,and parking error as optimization objects,a multi-objective model for train speed curve optimization is established.To simplify the calculation,the entropy weight method is used to solve the weight of each performance index in the multi-objective model.Secondly,based on the establishment of the train speed curve optimization model,an improved genetic algorithm is proposed to optimize the train operation strategy.The innovation of the algorithm is that the weights of the constraints and the values of the crossover and mutation factors can be adjusted adaptively to improve the algorithm's solution speed and its solution ability.Simulation experiments show that each performance index of the optimized speed curve is better than the traditional algorithm.Thirdly,according to the characteristics of large lag and multiple constraints of suspended permanent magnet maglev train,this paper introduces the predictive control algorithm,sets the input of the controller as the mixed speed of the output of the predictive controller and the target speed according to a certain weight,and uses the online optimization algorithm to solve its weight.The algorithm effectively alleviates the tracking error caused by the time delay of the suspended permanent magnet maglev train speed tracking controller,improves the dynamic performance of the train speed tracking controller,realizes its accurate and efficient operation,and enables the train to have a better speed tracking effect.Finally,the MATLAB simulation experiment platform for train speed curve tracking control is designed.This paper makes a detailed experimental analysis on the suspended permanent magnet maglev train under various working conditions such as different running time,different speed limit and large disturbance.The experimental results show that the control algorithm has the advantages of low energy consumption,high tracking accuracy,good passenger comfort and small parking error.Based on the above research content,this paper proposes a simple,efficient,and easy-toengineer method to obtain the optimized train speed curve,which provides the tracking object for the subsequent train speed curve tracking controller.At the same time,the train speed curve tracking control algorithm designed in this paper can effectively reduce the tracking error caused by the train time delay,and achieve more accurate and smooth tracking of the train target speed.The research in this paper has high realizability as a whole,which plays a positive role in promoting the realization of the automatic driving mode of suspended permanent magnet maglev train.