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基于高增益观测器和径向基函数神经网络的横向磁通永磁电机建模与输出跟踪

Modeling and output tracking of transverse flux permanent magnet machines using high gain observer and RBF neural network.

作者信息

Karimi H R, Babazadeh A

机构信息

Control and Intelligent Processing Center of Excellence, Department of Electrical and Computer Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.

出版信息

ISA Trans. 2005 Oct;44(4):445-56. doi: 10.1016/s0019-0578(07)60052-4.

DOI:10.1016/s0019-0578(07)60052-4
PMID:16294772
Abstract

This paper deals with modeling and adaptive output tracking of a transverse flux permanent magnet machine as a nonlinear system with unknown nonlinearities by utilizing high gain observer and radial basis function networks. The proposed model is developed based on computing the permeance between rotor and stator using quasiflux tubes. Based on this model, the techniques of feedback linearization and Hinfinity control are used to design an adaptive control law for compensating the unknown nonlinear parts, such as the effect of cogging torque, as a disturbance is decreased onto the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method in tracking both the angle and the angular velocity is shown in the simulation results.

摘要

本文利用高增益观测器和径向基函数网络,对横向磁通永磁电机作为具有未知非线性的非线性系统进行建模和自适应输出跟踪。所提出的模型是基于使用准磁通管计算转子和定子之间的磁导而开发的。基于该模型,采用反馈线性化和H无穷控制技术设计自适应控制律,以补偿未知非线性部分,如齿槽转矩的影响,作为干扰减小到转子角度和角速度跟踪性能上。最后,仿真结果表明了所提方法在跟踪角度和角速度方面的能力。

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