In this paper, we investigate the problem of Neural Network (NN) observer for nonlinear systems. Therefore, it can be applied to systems with higher degree of nonlinearity with any a priory knowledge about system dynamics. The proposed neuro-observer is a three-layer feedforward neural network, which is trained extensively with the error backpropagation learning algorithm including a correction term to guarantee good tracking as well as bounded NN weights. Furthermore, the Lyapunov’s direct method is used in order to ensuring the stability of the proposed non-conventional observer and of the NN weight errors. The effectiveness of the proposed state observer scheme is demonstrated through numerical simulation to reconstruct the unavailable state variables of an induction motor (IM) and especially the rotor flux despite the effect of the arisen parameters such as the load torque which is also reconstructed using the NN observer.
목차
Abstract 1. Introduction 2. Artificial neural network architecture 3. Neural network observer of nonlinear systems 4. Stability analysis of the neural network observer 5. Application of the proposed neuro-observer to the rotor flux observationof an induction motor 5.1. Induction motor model 5.2 Neural Network flux observer of induction motor 6. Conclusion References
보안공학연구지원센터(IJCA) [Science & Engineering Research Support Center, Republic of Korea(IJCA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Control and Automation
간기
월간
pISSN
2005-4297
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Control and Automation vol.3 no.1