基于模糊神经网络逆系统的无轴承永磁同步电机解耦控制

Decoupling Control of Bearingless Permanent Magnet Synchronous Motor Based on Inverse System Using the Adaptive Neural-fuzzy Inference System

无轴承永磁同步电机是一个多变量、非线性和强耦合的系统,因此实现转矩和径向悬浮力的动态解耦是无轴承永磁同步电机实现稳定高速高精度运行的关键。提出一种基于模糊神经网络逆系统的新型解耦控制方法,介绍了无轴承永磁同步电机基本结构和工作原理基础,并建立无轴承永磁同步电机转矩和悬浮力的数学模型。在对数学模型进行可逆性分析的基础上,采用模糊神经网络构建一个有效的逆系统,通过将逆系统与原系统串联,使原非线性系统解耦为3个单输入–单输出子系统,并同时设计了闭环控制器。对所设计的控制系统进行仿真和实验研究。仿真和实验结果表明,这种解耦控制方法可以实现无轴承永磁同步电机转矩和悬浮力之间的解耦控制,并具有良好的动态性能和稳定性。

A bearingless permanent magnet synchronous motor(BPMSM) is a multi-variable, nonlinear and strong coupling system, so it is necessary to realize dynamic decoupling control among torque and suspension forces for the stable, high speed and high precision operation of the BPMSM.In this paper, a novel decoupling control method based on the inverse system using the adaptive neuro-fuzzy inference system(ANFIS) was proposed. Firstly, the basic structure and working principle of the BPMSM were introduced, and the mathematical models of the suspension forces and torque were established. Secondly, based on the reversibility analysis of the mathematical models, the ANFIS was applied to build an effective inverse system of the BPMSM. Thirdly, by connecting the inverse system in series with the original system,the nonlinear original system was pseudo-linearized into three single input single output(SISO) subsystems, and additional closed-loop controllers were designed. Finally, the simulations and experiments were carried out. The results show that, the proposed control method can realize the decoupling control among torque and suspension forces of the BPMSM and it has good dynamic performance and stability.