自动化学报2013，Vol.39Issue(4)：433-439,7.DOI:10.3724/SP.J.1004.2013.00433
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
摘要
Abstract
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 ath-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Key words
Bearingless induction motor/ neural network ath-order inverse system theory/ internal model control/decoupling引用本文复制引用
王正齐,刘贤兴..基于神经网络逆系统的无轴承异步电机非线性内模控制[J].自动化学报,2013,39(4):433-439,7.基金项目
国家自然科学基金(61174055),南京工程学院校级科研基金(YKJ201217)资助 （61174055）
