Fuzzy T-S model-based design of min-max control for uncertain nonlinear systems

Tatjana Kolemishevska-Gugulovska, Mile Stankovski, I. Rudas, Nan Jiang, Juanwei Jing

Research output: Chapter in Book/Report/Conference proceedingChapter


The min-max robust control synthesis for uncertain nonlinear systems is solved using Takagi-Sugeno fuzzy model and fuzzy state observer. Existence conditions are derived for the output feedback min-max control in the sense of Lyapunov asymptotic stability and formulated in terms of linear matrix inequalities. The convex optimization algorithm is used to obtain the minimum upper bound on performance and the optimum parameters of min-max controller. The closed-loop system is asymptotically stable under the worst case disturbances and uncertainty. Benchmark of inverted pendulum plant is used to demonstrate the robust performance within a much larger equilibrium region of attraction achieved by the proposed design.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages18
Publication statusPublished - Jan 1 2017

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860949X


ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Kolemishevska-Gugulovska, T., Stankovski, M., Rudas, I., Jiang, N., & Jing, J. (2017). Fuzzy T-S model-based design of min-max control for uncertain nonlinear systems. In Studies in Computational Intelligence (Vol. 657, pp. 85-102). (Studies in Computational Intelligence; Vol. 657). Springer Verlag. https://doi.org/10.1007/978-3-319-41438-6_6