Stability analysis of nonlinear multivariable Takagi-Sugeno fuzzy control systems

Cuesta, Federico; Gordillo, Francisco; Aracil, J.; Ollero, A.

doi:10.1109/91.797976

Cited by 118 publications

(61 citation statements)

References 26 publications

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“…One is called the indirect adaptive FNN control and the other is called the direct adaptive FNN control [7], [8] (11) by the fuzzy logic system , , and , respectively. Therefore, the error dynamics (11) can be rewritten as (20) Let , then using (12) and (20) (23) Therefore, we always have , by using the supervisory control [see (22)]. Because , the bound of implies the bound of , which in turn implies the bound of .…”

Section: Hybrid Direct/indirect Adaptive Fnn Controller With Obsementioning

confidence: 99%

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

Wang

Lin

Lee

et al. 2002

IEEE Trans. Syst., Man, Cybern. B

124

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Section: Hybrid Direct/indirect Adaptive Fnn Controller With Obsementioning

confidence: 99%

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

Wang

Lin

Lee

et al. 2002

IEEE Trans. Syst., Man, Cybern. B

124

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“…This approach is pursued in the early works [29,30] and some indices were also derived, based on Jacobian matrix, to indicate how far from instability a fuzzy loop is [31,32].…”

Section: Model-based Fuzzy Control Designmentioning

confidence: 99%

Fuzzy control turns 50: 10 years later

Guerra

Sala

Tanaka

2015

Fuzzy Sets and Systems

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In 2015, we celebrate the 50th anniversary of Fuzzy Sets, ten years after the main milestones regarding its applications in fuzzy control in their 40th birthday were reviewed in FSS, see [1]. Ten years is at the same time a long period and short time thinking to the inner dynamics of research. This paper, presented for these 50 years of Fuzzy Sets is taking into account both thoughts. A first part presents a quick recap of the history of fuzzy control: from model-free design, based on human reasoning to quasi-LPV (Linear Parameter Varying) model-based control design via some milestones, and key applications. The second part shows where we arrived and what the improvements are since the milestone of the first 40 years. A last part is devoted to discussion and possible future research topics.

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“…Stability of T-S model fuzzy systems is quite important for practical applications. It has been widely studied by many authors, see, for examples, [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis for Uncertain Delayed T-S Fuzzy Systems

Yu¹,

Liao²

2014

2014 International Conference on Automatic Control Theory and Application

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Abstract-This paper studies the global exponential stability for a Takagi-Sugeno (T-S) fuzzy system with bounded uncertain delays. Most existing T-S method represent global nonlinear systems by connecting local linear systems with linguistic description. However, many complex systems cannot be represented by linear systems. In this paper, a class of local nonlinear systems having nice dynamic properties is employed to represent some global complex systems. Moreover, the delays are any uncertain bounded continuous functions. Sufficient conditions for global exponential stability of these delayed global complex systems are derived. Criteria for design of nonlinear fuzzy controllers to feedback control the stability of global nonlinear fuzzy systems are given.

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Stability analysis of nonlinear multivariable Takagi-Sugeno fuzzy control systems

Cited by 118 publications

References 26 publications

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

Fuzzy control turns 50: 10 years later

Stability Analysis for Uncertain Delayed T-S Fuzzy Systems

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