基于神经网络逆系统的无轴承异步电机非线性内模控制

王正齐; 刘贤兴

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

doi: 10.3724/SP.J.1004.2013.00433

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

详细信息

通讯作者:
王正齐

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- 被引次数: 0
出版历程
- 收稿日期: 2011-04-06
- 修回日期: 2011-12-02
- 刊出日期: 2013-04-20

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

1.
School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167;

摘要

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.
- 无轴承异步电机 /
- 神经网络 α阶逆系统方法 /
- 内模控制 /
- 解耦
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 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 anti-jamming ability of the closed-loop system. The effectiveness and superiority of the proposed strategy are demonstrated by simulation and experiment.
- Bearingless induction motor /
- neural network αth-order inverse system theory /
- internal model control /
- decoupling

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参考文献(1)

[1]

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