基于Backstepping理论的直线牵引电机无速度传感器控制

Speed-sensorless Vector Control of Linear Induction Motor Drives System Based Backstepping Control Theory

在动态边端效应的作用下,直线牵引电机参数会发生显著地变化,导致无速度传感器控制性能恶化。该文以中低速磁悬浮列车直线电机为研究对象,利用backstepping控制理论和MRAS相结合的速度辨识方案实现高性能直线电机无速度传感器矢量控制。首先,给出以定子电流、转子磁链为状态变量的直线电机状态空间模型;然后,在状态空间模型的基础上引入附加状态变量,改写直线电机状态方程;利用backstepping控制理论设计含有校正项的直线电机观测器模型,并以观测器模型作为可调模型,以电机实际模型作为参考模型,建立速度辨识的模型参考自适应系统,并采用李雅普洛夫稳定性理论得到速度辨识算法。最后,对该方案进行仿真及硬件在环测试,结果验证了该方案的可行性。

Thespeed-sensorlessvectorcontrol performance of linear induction motor(LIM)drive system is deteriorated because of the significant parameter variations of LIM associated with the dynamic end effects.Aiming at realizing high performance of the LIM speed-sensorless vector control system,a speed estimation scheme by combing backstepping control theory with model reference adaptive system(MRAS)theory in the sensorless-vector-controlled linear induction motor drives for medium-low speed maglev applications was proposed.Firstly,a state space-vector model of the LIM considering the dynamic end effects was shown in detail by regarding the stator current and rotor flux as the state variables;then,the state equations of the LIM were rearranged with additional state variables.The observer model of the LIM with correction terms was designed based on backstepping control theory,which was considered as the adaptive model of MRAS-based speed estimator.Correspondingly,the actual LIM model was used to replace the reference model of the MRAS-type speed estimation scheme.The Lyapunov stability theory was adopted for the realization of the speed estimation algorithm.The effectiveness and feasibility of the proposed speed estimation algorithm have been verified by simulation and hardware-in-the-loop(HIL)tests.The test results verify the validity of the proposed speed estimation scheme.