Robust adaptive control using fuzzy-neural controller
- Authors
- Seo, Jae-Yong; Kim, Seong-Hyun; Jeon, Hong-Tae
- Issue Date
- Aug-1999
- Publisher
- IEEE, Piscataway, NJ, United States
- Citation
- IEEE International Conference on Fuzzy Systems, v.3, pp III - 1305 - III-1308
- Journal Title
- IEEE International Conference on Fuzzy Systems
- Volume
- 3
- Start Page
- III
- End Page
- 1305 - III-1308
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56564
- ISSN
- 0000-0000
- Abstract
- This paper proposes an adaptive fuzzy-neural control scheme that yields robust trajectory tracking in the presence of parametric and unstructured uncertainty. The uncertainties include bounded disturbances, dynamic-parametric changes as well as unmodeled dynamics which is dependent on state variables. The proposed method employs fuzzy-neural controller to compensate for uncertain nonlinearity of dynamic system in the traditional direct MRAC system. To improve the robustness of adaptive fuzzy controller and diminish the tracking error boundary, a robust adaptive law is derived from the Lyapunov stability technique and switching σ-scheme, usually applied to adaptive control. Combining fuzzy-neural theory and adaptive control technique, the proposed control will provide better robust tracking control performance than a traditional MRAC.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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