Fuzzy Guaranteed Cost Control for Nonlinear systems

Boukas, El‐Kébir

doi:10.1109/nafips.2006.365422

Cited by 6 publications

(18 citation statements)

References 16 publications

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“…Therefore, guaranteed-cost control with input constraint has been proposed for a class of chaos synchronization [22]. Although [20] proposed a guaranteed-cost control method for a T-S fuzzy system with model uncertainty, it does not consider input/state constraints. Although [20] proposed a guaranteed-cost control method for a T-S fuzzy system with model uncertainty, it does not consider input/state constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Although [20] proposed a guaranteed-cost control method for a T-S fuzzy system with model uncertainty, it does not consider input/state constraints. Nevertheless, if the methods of [20] and [5] are combined to solve the problem of guaranteed-cost control with input/state constraints for the T-S fuzzy system with model uncertainty, the guaranteed-cost value must be smaller than 1. Nevertheless, if the methods of [20] and [5] are combined to solve the problem of guaranteed-cost control with input/state constraints for the T-S fuzzy system with model uncertainty, the guaranteed-cost value must be smaller than 1.…”

Section: Introductionmentioning

confidence: 99%

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Guaranteed‐cost fuzzy controller design for a self‐sustaining bicycle with practical constraints

Chen

Wang

Chang³

2011

Asian Journal of Control

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This study presents a guaranteed-cost fuzzy controller for a selfsustaining bicycle. First, the nonlinear dynamics of the bicycle are exactly transformed into a T-S fuzzy system with model uncertainty. The guaranteedcost fuzzy controller is then designed for the transformed T-S fuzzy system. For practical considerations, the input/state constraints are also satisfied in the design. The main contribution of this study is the guaranteed-cost control design for a T-S fuzzy system with model uncertainty and input/state constraints. Finally, simulation results show the validity of the proposed controller design method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Guaranteed‐cost fuzzy controller design for a self‐sustaining bicycle with practical constraints

Chen

Wang

Chang³

2011

Asian Journal of Control

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“…Computational development has allowed the use of much more complex techniques to deal with the nonlinear problem. Among those techniques one can highlight the use of fuzzy logic [1] [2] and neural networks.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Adaptive Controller

Cavalcante¹,

Schnitman²,

Loureiro³

2008

2008 IEEE Latin American Robotic Symposium

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In this work a controller scheme is applied to a class of nonlinear systems in the formẋ = f (x) + g(x)u. In this method fuzzy estimators are used to estimate the function f (x) and g(x). The fuzzy system is a zero-order takagi-sugeno-kang structure. The methology guarantees that the fuzzy estimates f (x| θ f ) and g(x| θ g ) converge to their optimal value f (x|θ * f ) and g(x|θ * g ). The method is applied to an omnidirectional robot for practical results.

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“…One approach to this problem is the so called guaranteed cost control approach in which a fixed quadratic Lyapunov function is used to establish an upper bound on the closed-loop value of an integral quadratic cost function. Recently, there are many results on fuzzy guaranteed cost control for normal nonlinear systems [4][5][6][7][8]. E. Boukas proposed a new approach to develop the results on fuzzy guaranteed cost control [4].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, there are many results on fuzzy guaranteed cost control for normal nonlinear systems [4][5][6][7][8]. E. Boukas proposed a new approach to develop the results on fuzzy guaranteed cost control [4]. Bing Chen et al investigated guaranteed cost control for T-S fuzzy systems with state and input delays and also derived stability and stabilization conditions which were delay-dependent for state delay and delayindependent for input delay [5].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Guaranteed Cost Control for Uncertain Singular Systems with State and Input delays

Yang¹,

Huang²,

Wang³

2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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Based on T-S fuzzy model, the robust guaranteed cost control problem is studied for a class of uncertain singular systems with state and input delays. Sufficient conditions are provided for the construction of a state feedback guaranteed cost controller. These conditions are given in terms of the feasibility of linear matrix inequalities (LMIs) and guarantee that the closed-loop systems are quadratically stable. Moreover, an upper bound of the guaranteed cost is also obtained. A numerical example is provided to demonstrate the effectiveness of the proposed method.

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Fuzzy Guaranteed Cost Control for Nonlinear systems

Cited by 6 publications

References 16 publications

Guaranteed‐cost fuzzy controller design for a self‐sustaining bicycle with practical constraints

Guaranteed‐cost fuzzy controller design for a self‐sustaining bicycle with practical constraints

An Intelligent Adaptive Controller

Fuzzy Guaranteed Cost Control for Uncertain Singular Systems with State and Input delays

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