Research On Speed Curve Optimization And Tracking Control Of Maglev Train

With the increasing maturity of maglev technology,maglev trains are gradually becoming a new rail transportation mode.The maglev train has the advantages of safety and comfort,energy saving and high efficiency,and better practicality.Due to the existence of many factors such as a large time delay,nonlinearity and many constraints in the maglev train operation control system,it is often difficult for the traditional control algorithm to realize the speed curve optimization and speed-tracking control of the automatic train operation system.To address the above problems,this paper proposes a train operation speed optimization method based on an improved artificial bee colony and particle swarm algorithm to obtain a target speed curve with better performance;and a nonlinear active disturbance rejection controller based on the intelligent optimization of the parameters of the adaptive particle swarm algorithm is designed to achieve accurate tracking of the target speed curve of the train.The research work carried out in this paper is as follows:(1)First,the single mass point dynamics model of the maglev train is established through the force analysis of the maglev train in operation;Secondly,the combined forces on the maglev train under different operating conditions are also calculated,and different operating control strategies are analyzed;Finally,under the condition of satisfying the operating speed constraint,the four performance indexes of accurate parking,punctuality,passengers’ comfort and operating energy consumption are taken as the optimization objects,and establish a multi-objective optimization model for the maglev train.(2)To address the problems of insufficient optimization,slow convergence speed and low convergence accuracy of traditional optimization algorithms in realizing speed curve optimization,this paper proposes an improved artificial bee colony and particle swarm fusion algorithm(IABCPSO)for the maglev train speed curve optimization.The improvement of the artificial bee colony algorithm mainly lies in the increase of the population initialization comparison and the adjustment of the position updating formula.The fusion algorithm solves the problems that the artificial bee colony algorithm(ABC)converges slowly and the particle swarm algorithm(PSO)early convergence.The simulation results show that the speed curve optimized by the IABC-PSO algorithm is better than the PSO algorithm in all performance indexes.(3)To address the problems of insufficient tracking accuracy and poor anti-disturbance capability of traditional control algorithms in realizing speed tracking control,this paper proposes a nonlinear active disturbance rejection controller based on adaptive particle swarm algorithm(APSONLADRC)for speed tracking control of the maglev train.A third-order extended state observer is used to enhance the NLADRC anti-disturbance capability,and the APSO algorithm is used to intelligently optimize the NLADRC controller parameters.The simulation and comparison experiments show that the APSO-NLADRC control algorithm has the advantages of high tracking accuracy,small speed error,small acceleration error and good passengers’ riding comfort.