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access icon free Stochastic sampled-data controller for T–S fuzzy chaotic systems and its applications

© The Institution of Engineering and Technology
Received 31/08/2018, Accepted 29/04/2019, Revised 13/04/2019, Published 01/05/2019

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.

Inspec keywords: stability; Lyapunov methods; delays; nonlinear control systems; fuzzy systems; asymptotic stability; linear matrix inequalities; control system synthesis; fuzzy control; sampled data systems; stochastic systems; time-varying systems

Other keywords: randomly time-varying; chaotic Takagi–Sugeno fuzzy systems; stabilisation conditions; exponential stabilisation; stochastic sampled-data controller; input delays; stability; control scheme; random time delay; chaotic nature; T–S fuzzy models; T–S fuzzy chaotic systems; dynamical systems; existing controller schemes

Subjects: Control system analysis and synthesis methods; Algebra; Discrete control systems; Stability in control theory; Fuzzy control; Distributed parameter control systems; Other topics in statistics; Time-varying control systems; Nonlinear control systems

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