Robust stabilisation of non-linear uncertain Takagi–Sugeno fuzzy systems by H∞ control

“…However, it is necessary to study the systems with time-varying delays [23], random delays [24], distributed delays [25], etc. due to the existence of more complex systems.…”

Section: Remarkmentioning

confidence: 99%

Stochastic H2/H∞ control of nonlinear systems with time-delay and state-dependent noise

Gao

Sheng

Zhang

2015

Applied Mathematics and Computation

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“…The Takagi-Sugeno (T-S) fuzzy model-based approach has received great attention for modeling nonlinear systems [17,18,20,24,30,43,45,47,49,52]. The main reason is that it has been proved to be able to approximate any smooth nonlinear function to any degree of accuracy [1,40].…”

Section: Introductionmentioning

confidence: 99%

Polynomial-Approximation-Based Control for Nonlinear Systems

Zhou

Chen

Lam

2016

Circuits Syst Signal Process

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This paper is concerned with the stabilization problem for nonlinear systems. A new polynomial-approximation-based approach for modeling nonlinear systems is first proposed. The nonlinearity is approximated by polynomials and the approximation errors are treated as modeling uncertainties. The original nonlinear systems are converted into polynomial systems with modeling uncertainties. In order to highlight the approximation accuracy, the piecewise polynomial approximation functions are utilized. A novel polynomial state-feedback controller is designed to solve the stabilization problem. Furthermore, switched polynomial state-feedback controllers are designed to improve the performance. The stabilization conditions are presented in terms of sum of squares, which can be numerically solved via SOSTOOLS. Finally, simulation examples are provided to demonstrate the feasibility of the proposed method and show its advantage over the polynomial-fuzzy-model-based approach.

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“…Based on the universal approximation theorem, fuzzy systems have supplanted conventional technologies in many scientific applications and engineering systems such as Takagi-Sugeno fuzzy systems H? control [19] and stability and stabilization criteria for fuzzy neural networks [20]. The fuzzy modeling and synchronization of different memristor-based chaotic circuits is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

Synchronization of Fuzzy Modeling Chaotic Time Delay Memristor-Based Chua’s Circuits with Application to Secure Communication

Lin

Huang

et al. 2015

Int. J. Fuzzy Syst.

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In this paper, synchronization of fuzzy modeling chaotic time delay memristor-based Chua's circuits is presented. Based on T-S fuzzy models, not only fuzzy model of the time delay memristor-based Chua' circuit is constructed but the fuzzy control vector can also be derived to synchronize two different time delay memristor-based Chua's circuits. Due to the dynamical behavior with complex transient transitions of the memristor-based chaotic system which is heavily dependent on the initial state of the memristor except for the circuit parameters, the memristor-based chaotic system can generate more complex and unpredictable time domain signals. An application to chaos secure communication is used to demonstrate the effectiveness of the proposed chaotic synchronization scheme.

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Robust stabilisation of non-linear uncertain Takagi–Sugeno fuzzy systems by H∞ control

Cited by 42 publications

References 26 publications

Stochastic H2/H∞ control of nonlinear systems with time-delay and state-dependent noise

Stochastic H2/H∞ control of nonlinear systems with time-delay and state-dependent noise

Polynomial-Approximation-Based Control for Nonlinear Systems

Synchronization of Fuzzy Modeling Chaotic Time Delay Memristor-Based Chua’s Circuits with Application to Secure Communication

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