Research On Suspension Control Strategy Of Medium And Low Speed Maglev Train Based On Feedback Linearization

The Maglev Train has the advantages of low vibration noise,strong climbing ability,low cost and low pollution.At present,with the continuous improvement of people's requirements for traffic speed and ride quality,Maglev Trains will have wide development space in the future development and application of railways.One of the core and key technologies of Maglev Trains is the suspension control technology.The stability of the suspension control is the basis and prerequisite for the normal operation of Maglev Trains.The traditional electromagnetic suspension of Maglev Trains basically adopts the lead-lag compensation technology of the classical control theory,so that the electromagnet is dynamically stabilized on a certain air gap.This can produce a stable magnetic float in most cases,but it is not enough for the damping,stability margin,and anti-disturbance required by the manned vehicle.In order to improve the suspension control stability and control response speed of the maglev train,a suspension controller based on model predictive control is presented.Firstly,it briefly introduces the basic working principle of Maglev Train suspension system,analyzes the stability of the mathematical model of the suspension system,and linearizes the nonlinear unstable suspension system.The suspension linear controller is designed according to the two linearization methods to consider the influence of different interference factors on the suspension control.By comparing the controller performance of feedback linearization and equilibrium point approximation linearization design,the disadvantages of traditional suspension control algorithm are summarized,which is a theoretical foundation for the proposed predictive control algorithm of suspension system model.Then,based on the feedback linearization of the suspension system,the predictive control is carried out,and the predictive model of the suspension system is obtained by discrete processing of the linearized system.According to the basic principle of the model predictive control,the multi-step prediction equation of the suspension system is established.Based on optimizing the prediction model by rolling the optimization function,the optimal solution of the prediction equation is obtained,and the obtained optimal control input is applied to the suspension control system to obtain the desired optimal output value.By comparing and analyzing the control effects of three control algorithms,it is concluded that the suspension predictive control has some advantages of fast dynamic response,strong anti-interference ability,small steady-state error,and little influence from the system dynamic model and parameter changes.Finally,considering the actual operation of Maglev Trains,the sensor measurement signal of the suspension system is filtered,and the state of the system unmeasurable state is estimated.By designing a Kalman filter in the suspension control system,the sensor measurement error and the unknown disturbance problem of the control process are effectively solved,and the optimal estimation of the unmeasured state quantity is provided for the suspension control system.