永磁同步电机的神经网络逆动态解耦控制

永磁同步电机是一个非线性、强耦合系统，应用神经网络逆系统方法对永磁同步电机进行动态解耦控制研究。通过对永磁同步电机的数学模型可逆性分析，得出解析逆系统，由解析逆系统与永磁同步电机原系统复合成两个伪线性子系统来构造神经网络逆系统，使永磁同步电机动态解耦成二阶线性转速子系统和一阶线性磁链子系统，并采用鲁棒伺服控制器对伪线性子系统进行线性闭环控制器的设计，实现永磁同步电机转速和定子磁链的动态解耦，仿真表明系统具有良好的动静态性能。

Dynamic decoupling Control of the PMSM Based on Neural Network Inverse Method

A decoupling control approach based on neural network inverse system was developed for the permanent magnet synchronous motor (PMSM), which was a nonlinear and high coupling system. The analytical inverse system of PMSM was obtained by analyzing the reversibility of the mathematical model. The radical structure of neural network inverse system is constituted by two pseudo-linear subsystems that combine analytical inverse system and primordial system of the permanent magnet synchronous motor. The permanent magnet synchronous motor is decoupled into second-order linear speed subsystem and first-order linear flux subsystem The dynamic decoupling control between speed and flux of PMSM were realized by using the servo robust controller to design linear closed-loop controller for the pseudo-linear subsystems. Simulation results show that the whole control system has good dynamic and static performance.