CAJ | 학술논문

永磁同步电机(PMSM)是一种非线性、强耦合的控制对象,电机参数的变化加大了其控制难度.因此,参数辨识对于其闭环控制系统的稳定运行有着重大的意义.文中针对这一非线性、强耦合的模型,研究了一种基于扩展卡尔曼滤波(EKF)和Elman神经网络(Elman NN)的永磁同步电机参数R_s,ψ_d和ψ_q的辨识方法.仿真结果表明,该方法具有很快的收敛速度,能很精确地辨识PMSM的R_s,ψ_d和ψ_q,该网络具有良好的泛化能力,在变速变负载等复杂情况下也适用.
영자동보전궤(PMSM)시일충비선성、강우합적공제대상,전궤삼수적변화가대료기공제난도.인차,삼수변식대우기폐배공제계통적은정운행유착중대적의의.문중침대저일비선성、강우합적모형,연구료일충기우확전잡이만려파(EKF)화Elman신경망락(Elman NN)적영자동보전궤삼수R_s,ψ_d화ψ_q적변식방법.방진결과표명,해방법구유흔쾌적수렴속도,능흔정학지변식PMSM적R_s,ψ_d화ψ_q,해망락구유량호적범화능력,재변속변부재등복잡정황하야괄용.
Permanent magnet synchronous motor (PMSM) is a complex plant to control,due to its high nonlinearity and strong coupling. At the same time, the variation of motor parameters make this problem more serious. So, parameter identification of PMSM seems to be important for the two closed-loop vector control system.To solve this problem, a new method combining elman neural network(ENN) and modified extended kalman filter(EKF) is studied in this paper. The approach of identifying R_s,ψ_d and ψ_q is discussed.Simulation results show that it has lots of advantages such as high precision,fast convergence and excellent generalization ability and it is suitable for variable speed and load disturbance,even more complex circumstance.