Earticle

초록

영어

A SVD-Krylov method for large scale MIMO(multi-input multi-output) system model-order reduction is proposed in this paper. Its aim is to combined the singular value decomposition(SVD)and Krylov methods by retaining the best feature that can be applied for solving some problems for the large scale system model-order reduction. The method matches the first r Markov parameters and minimizes the error in the remaining ones in the least-squares sense. The reduced model is asymptotically stable, matches a certain number of moments, and minimizes a weighted error in the discrete time case. The effectiveness of the proposed approaches is tested by the Iss (international space station)model that in the SLICOT library, getting the frequency-response, the error and the error bounds of different order reduced model. The result shows that the proposed method is efficiently.

목차

Abstract
 1. Introduction
 2. The Basic Theory of Krylov Model Order Reduction Method
 3. The SVD Model Order Reduction Method
 4. The SVD-Krylov Model Order Reduction Method
 5. Model Reduction Example
 6. Conclusion
 References

키워드

저자

• Zhe Yan [ professor at the department of automation from Harbin University of Science and Technology ]
• Fangming Lu [ research area mainly includes control theory and applications ]
• lin Zhou [ research area mainly includes control theory and applications ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605