| With the advantages of high speed,high safety,superior acceleration performance and climbing ability,low noise,energy saving and environmental protection,low maintenance cost and so on,high-speed maglev traffic has broad application prospects.The train operation control system is an important subsystem of the high-speed maglev train,and the reliable operation control algorithm is the guarantee for safe and efficient operation of the train.However,at present,mature operation control algorithms are lacked in high-speed maglev train system.The high-speed maglev train will be disturbed by operation resistances such as air resistance and magnetic resistance during operation.At the same time,controller input saturation is a common nonlinear constraint,which will lead to a decline in the system control performance.Therefore,the design of the compensation algorithm for operation resistance and input saturation is of great significance to improve the operation control performance of the high-speed maglev train with input saturation.Aiming at the high-speed maglev train running periodically back and forth on a fixed line,this thesis comprehensively considers the influence of train operation resistance and controller input saturation,combines the periodic adaptive learning control(PALC)algorithm and the input saturation compensation method based on the auxiliary system,and designs a periodic adaptive learning control-input saturation compensation(PALC-IS)based maglev train operation control algorithm.The PALC-IS control algorithm effectively reduces the adverse effects of the operation resistance and input saturation on the train operation control performance,and improves the position and speed tracking performance of the maglev train.The specific work of this thesis is as follows:Firstly,on the basis of the electromagnetic characteristics of long stator permanent magnet linear synchronous motor(PMLSM)and the force condition of the train,the dynamic model of the high-speed maglev train operation control system is established.Secondly,a train operation controller based on the PALC algorithm is designed by considering the periodic characteristics of the train operation resistance.The Lyapunov stability theory is used to prove the system stability based on the PALC controller.Comparison simulations with model reference adaptive controller(MRAC)and proportional-integral-derivative controller(PID)are conducted and the simulation results show that the proposed PALC controller can accurately estimate the train operation resistance and improve the tracking accuracy of position and speed,improving the operation control performance of the high-speed maglev train.Finally,in order to eliminate the adverse effects of input saturation on the control performance,the PALC-IS controller is designed by adding a saturation compensation module.The system stability based on PALC-IS controller is proved by the Lyapunov stability theory.The comparison simulations with the PALC controller shows that the PALC-IS controller has better anti-saturation effect,and can effectively improve the tracking accuracy of position and speed with lower energy cost when the controller input saturation occurs. |
