Earticle

초록

영어

A novel static decoupling control strategy based on Hammerstein model and neural network for induction motors was proposed in this paper. Hammerstein model, consisting of a static nonlinear module and a dynamic linear module, can be used to model many nonlinear systems. In the proposed method, firstly, neural network and auto-regressive moving-average (ARMA) model were employed to construct the static nonlinear module and the dynamic linear module respectively. Further, neural network inverse model of the static nonlinear module can be trained on the static dataset collected in the framework of the Hammerstein model. Finally, the inverse model was utilized to offset the nonlinear characteristic of an induction motor, decoupled into a rotor speed subsystem and a rotor flux subsystem. Simulations show that the proposed static decoupling control strategy has satisfactory decoupling performances and robustness to load disturbance in close loop control.

목차

Abstract
 1. Introduction
 2. Invertibility Analysis of Induction Motor Model
  2.1. Induction Motor Model
  2.2. Invertibility Analysis
 3. Static Decoupling Control of Induction Motors Based on Hammerstein Model and Neural Network
  3.1. Hammerstein Model based Decoupling Control Strategy
  3.2. Identification of Hammerstein Motor
  3.3. Inversion of Static Nonlinear Module
  3.4. Static Decoupling Control Strategy for Induction Motors
 4. Simulations
  4.1. Parameters of the Three Phase Induction Motor
  4.2. Dynamic Linear Module
 5. Conclusions
 References

키워드

저자

• Mei Congli [ School of Electronic and Information Engineering, Jiangsu University, Zhenjiang, China ]
• Yin Kaiting [ School of Electronic and Information Engineering, Jiangsu University, Zhenjiang, China ]
• Huang Wentao [ School of Electronic and Information Engineering, Jiangsu University, Zhenjiang, China ]
• Liu Guohai [ School of Electronic and Information Engineering, Jiangsu University, Zhenjiang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605