Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11021135 | Neurocomputing | 2018 | 9 Pages |
Abstract
This paper deals with a static output-feedback stabilization problem within a sampled-data framework for nonlinear systems in Takagi-Sugeno form. We investigate this problem on the basis of the descriptor-redundancy scheme. The resulting features are that: (i) the sampled-data synthesis does not involve (even approximate) discrete-time models of nonlinear systems; (ii) the sampled-data stability is analyzed in the continuous-time Lyapunov sense; and (iii) the design algorithm consists of a single-stage linear matrix inequality problem without linear matrix equality constraints.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hyoung Bin Kang, Ho Jae Lee,