Stabilization of feedback linearizable systems using a radial basis function network
SCIE
SCOPUS
- Title
- Stabilization of feedback linearizable systems using a radial basis function network
- Authors
- Nam, K
- Date Issued
- 1999-05
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGI
- Abstract
- The main obstacle in the practical use of the feedback linearization is the difficulty in obtaining a linearizing feedback and a coordinate transformation map. Finding a desired transformation map and feedback turns out to be finding an integrating factor for an annihilating one-form. In this work, we develop numerical algorithms for an integrating factor and the corresponding zero-form. Employing a radial basis function (RBF) neural network as an interpolation method for the data resulted from the numerical algorithms, the authors obtained an approximate integrating factor and zero-form in closed forms. Finally, they construct a stabilizing controller based on a linearized system with the use of the approximate integrating factor and zero-form.
- Keywords
- approximate integrating factor; feedback linearization; RBF neural network; NEURAL NETWORKS; DYNAMIC-SYSTEMS
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/20413
- DOI
- 10.1109/9.763222
- ISSN
- 0018-9286
- Article Type
- Article
- Citation
- IEEE TRANSACTIONS ON AUTOMATIC CONTROL, vol. 44, no. 5, page. 1026 - 1031, 1999-05
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- There are no files associated with this item.
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