Research On Robust Control Of Maglev Train Speed Based On Improved Active Disturbance Rejection Controller

With the advantages of safety and low carbon,quiet and comfortable,magnetic levitation rail transportation is a useful supplement to build a green and intelligent city transportation network in China,and has a broad application prospect in China.As an important function of intelligent driving of maglev trains,speed tracking control of maglev trains has an important impact on the operational safety,ride comfort and operational efficiency of maglev trains.Therefore,the reliability research of the speed tracking controller of maglev trains can effectively improve the reliability and competitiveness of maglev trains and solidify the development foundation of modern comprehensive transportation system in China.Accurate and smooth speed tracking control is one of the important technologies to ensure the safety and efficiency of the fully automated driving process of maglev trains.However,under the complex and changing operating environment factors and magnetic field force interference,the traditional train speed control method is difficult to guarantee the accurate and smooth speed tracking control of maglev trains.In this thesis,we study the speed tracking control of maglev trains,build a maglev train dynamics model,speed tracking controller and speed tracking control simulation model,and conduct simulation experiments.The main contents of this thesis are as follows:(1)A detailed analysis of the literature related to the operation control of maglev trains is presented,and a longitudinal dynamics model of maglev trains is established to effectively describe the nonlinear hysteresis characteristics of the train operation control process.A model of the traction and braking characteristics of the maglev train is given,and a detailed description of the key issues of the electro-hydraulic hybrid braking characteristics of the maglev train is given.The traction calculation model and energy consumption model of the maglev train are constructed,and finally,the target speed curve used in the simulation is introduced.(2)Under the interference of complex static magnetic field force and variable environmental factors,the traditional train speed control method is difficult to achieve accurate and smooth speed control of maglev trains.In this thesis,we propose a robust control method based on parameter self-tuning and self-anti-disturbance for speed tracking of maglev trains to solve the problem of accurate and smooth speed tracking control of maglev trains under complex disturbances in intelligent driving process.And the BP neural network PID controller and the conventional self-anti-disturbance controller are designed,and the two are compared and analyzed.(3)Based on the operating characteristics of maglev trains,a simulation model of maglev train speed tracking is built,and then a BP neural network control parameter estimation-based Active Disturbance Rejection Controller(BP-ADRC)is used for speed tracking experiments to realize the adaptive adjustment of the parameters of the ADRC expansion state observer during the train speed control.And it is compared and analyzed with PID,BP-PID and traditional self-anti-disturbance controller.The experimental data analysis shows that the speed tracking accuracy of the BP-ADRC controller proposed in this thesis is 32.87%,20.96% and 16.66% higher than the traditional PID,BP-PID and ADRC controllers,respectively,and the speed tracking smoothness is31.45%,19.78% and 16.69% higher,respectively.The experimental results show that the BPADRC controller designed in this thesis has significant advantages in improving the speed tracking control accuracy and robustness of maglev trains compared with the traditional train speed control methods,which verifies the effectiveness and feasibility of the modeling and control strategies proposed in this thesis.