基于神经网络逆系统的无轴承异步电机非线性内模控制  

Nonlinear Internal Model Control for Bearingless Induction Motor Based on Neural Network Inversion

作　　者：王正齐[1,2] 刘贤兴[2] 

机构地区：[1]南京工程学院电力工程学院,南京211167 [2]江苏大学电气信息工程学院,镇江212013

出　　处：《自动化学报》2013年第4期433-439,共7页Acta Automatica Sinica

基　　金：国家自然科学基金(61174055);南京工程学院校级科研基金(YKJ201217)资助~~

摘　　要：针对无轴承异步电机非线性、多变量、强耦合的特点,提出一种基于神经网络α阶逆系统方法的非线性内模控制策略.将用动态神经网络逼近的无轴承异步电机α阶逆模型与原系统复合,将非线性的无轴承异步电机原系统解耦成转子径向位移、转速和转子磁链四个独立的伪线性子系统.为了保证系统的鲁棒性,对伪线性系统引入内模控制,仿真和实验研究验证了所提控制方法的有效性.The bearingless induction motor is a nonlinear, multi-variable and strongly coupled system. For this system, a novel internal model control strategy based on neural network αth-order inverse system theory is proposed in this paper to realize the decoupling control. By cascading the αth-order inverse model approximated by the dynamic neural network with the original system, the nonlinear bearingless induction motor system is decoupled into four independent pseudo-linear subsystems, that is, two radial displacement subsystems, a speed subsystem and a rotor flux subsystem. Then, the internal model control method is introduced to the four pseudo-linear subsystems to ensure the robustness and antijamming ability of the closed-loop system. The effectiveness and superiority of the proposed strategy are demonstrated by simulation and experiment.

关 键 词：无轴承异步电机 神经网络α阶逆系统方法 内模控制 解耦 

分 类 号：TM343[电气工程—电机] TP183[自动化与计算机技术—控制理论与控制工程]