Open AccessStochastic sampled‐data controller for T–S fuzzy chaotic systems and its applications
Author(s) -
Gunasekaran Nallappan,
Joo Young Hoon
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.5971
Subject(s) - control theory (sociology) , controller (irrigation) , bernoulli's principle , chaotic , fuzzy logic , stability (learning theory) , mathematics , exponential stability , fuzzy control system , bernoulli distribution , linear matrix inequality , scheme (mathematics) , computer science , mathematical optimization , control (management) , random variable , artificial intelligence , engineering , nonlinear system , machine learning , agronomy , biology , mathematical analysis , statistics , physics , quantum mechanics , aerospace engineering
The present study mainly focuses on designing a stochastic sampled‐data controller for chaotic Takagi–Sugeno (T–S) fuzzy systems. Distinct to the existing controller schemes, in this work, the random time delay is introduced into the proposed control scheme that ensures the exponential stabilisation of T–S fuzzy models. In addition, the input delays are assumed to be randomly time‐varying, which copes with the traditional uncorrelated Bernoulli distributed sequences. Based on the proposed Lyapunov–Krasovskii functional and using new weighted integral inequalities, the stability and stabilisation conditions are derived and expressed in terms of linear matrix inequalities, which ensure the exponential stability of the states. Finally, in the simulation results, the chaotic nature of two dynamical systems are considered for validation of the derived conditions. From the simulation results, it is concluded that the proposed method can provide better stability performance and less conservative results.