Earticle

초록

영어

A novel control strategy based on Hammerstein model and neural network for the speed-regulating system of the induction motor and inverter is proposed in this paper. First, Hammerstein model was used to model the speed-regulation system of the induction motor and inverter. Auto-regressive and moving average (ARMA) model was used to identify the dynamic linear module of Hammerstein model of the speed-regulating system. Second, the ARMA model was used as a reference model for identification of the inverse model of static nonlinear neural network (NN) module of Hammerstein model in the framework of the model reference adaptive control method. For the load disturbance issue, two control strategies, online learning neural network direct inverse control and the traditional PI close-loop control strategy were studied. Simulations show that the inverse control based on Hammerstein model and NN is effective and the online learning neural network direct inverse control strategy for the speed-regulating system with load disturbance has higher performance.

목차

Abstract
 1. Introduction
 2. Speed-regulating System of the Induction Motor and Inverter
 3. Speed-regulating System for Induction Motor and Inverter based on Hammerstein Model and Neural Network
  3.1. Hammerstein Model
  3.2. Identification of the Linear Dynamic Module
  3.3. Identification of the Inverse Model of the Nonlinear Static Module
  3.4. Identification and Control of the Online Learning Neural Network Inverse Model based on Model-reference
  3.5. Identification and Control of the Traditional PI Close-loop
 4. Simulation
  4.1. Three Phase Induction Motor Parameters
  4.2. Simulation Results
 5. Conclusion
 References

키워드

저자

• Mei Congli [ School of Electronic and Information Engineering, Jiangsu University, Zhenjiang 212013, China ]
• Huang Wentao [ School of Electronic and Information Engineering, Jiangsu University, Zhenjiang 212013, China ]
• Yin Kaiting [ School of Electronic and Information Engineering, Jiangsu University, Zhenjiang 212013, China ]
• Liu Guohai [ School of Electronic and Information Engineering, Jiangsu University, Zhenjiang 212013, 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