• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.59-66
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• 2010

This paper is proposed an adaptive fuzzy bilinear synchronization design for uncertain $L\ddot{u}$ chaos system. It is assumed that the $L\ddot{u}$ chaos system has unknown parameters. First, The $L\ddot{u}$ chaos system can be reconstructed via TS fuzzy bilinear modeling. We design an adaptive fuzzy bilinear synchronization control scheme based on TS fuzzy bilinear $L\ddot{u}$ chaos system with uncertain parameters. Lyapunov theory is employed to guarantee the stability of error dynamic system between TS fuzzy bilinear $L\ddot{u}$ chaos system and the proposed slave system and to derive the adaptive laws for estimating unknown parameters. Simulation results is given to demonstrate the validity of our proposed synchronization scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.137-140
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• 2001

In this paper, we design 75(Takagi-Sugeno) fuzzy controllers for the systems that can be represented by the 75 fuzzy model. We use inverse optimal approach in which the cost function is determined later than the Lyapunov function and its corresponding control input satisfying the design requirements such as stability and decay rate. The obtained design procedure is in the form of solving LMI(Linear Matrix Inequalities), thus very efficient in practice.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1958-1960
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• 2001

In this paper, we propose the design methodology of target tracking system using the identification of TS fuzzy model based on genetic algorithm(GA) and RLS algorithm. In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. In this paper, to resolve these problems of nonlinear filtering technique, the error of EKF by nonlinearity is compensated by identifying TS fuzzy model. In the proposed method, after composing training datum from the parameters of EKF, by identifying the premise and consequent parameters and the rule numbers of TS fuzzy model using GA, and by tuning finely the consequent parameters of TS fuzzy model using recursive least square(RLS) algorithm, the error of EKF is compensated. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2630-2632
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• 2000

In this paper, a control technique of Takagi-Sugeno (TS) fuzzy systems with parametric uncertainties is developed. The uncertain TS fuzzy system is represented as an uncertain multiple linear system. The control problem of TS fuzzy system is converted into the stabilization problem of a uncertain multiple linear system. A sufficient condition for robust stabilization is obtained in terms of linear matrix inequalities (LMI). A Design example is illustrated to show the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2286-2288
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• 2003

In this paper, a novel and efficient discretization method of a continuous-time Takagi-Sugeno (TS) fuzzy system is proposed. Because of the highly complex and nonlinear interaction among the subsystems, it is very difficult to develop the discretized version of continuous-tine TS fuzzy system. More precisely, to obtain the suitable discretized version, it is necessary to solve two main problems: to discretize global state equations of the continuous-tine TS fuzzy system and to hold the polytopic structure after the discretization. This paper will show the solution to above two problems. Main key idea is to transforme the sampling period into the intersampling period. Finally, To show the feasibility and the validity of the proposed method, a computer simulation is provided.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.51-60
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• 1998

There have been several recent studies concerning the stability of fuzzy control system and the synthesis of stabilizing fuzzy controllers. This paper reports on a related study nf the TS (Takagi-Sugeno) fuzzy systems, and it is shown that the controller synthesis problems for the nonlinear systems described by the TS fuzzy model can be reduced to convex problems involving LMIs (linear matrix ineclualities). After classifying the TS fuzzy systems into three families based on how diverse their input matrices are, different controller synthesis procedure is given for each of these families. A numerical example is presented to illustrate the synthesis procedures developed in this paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.326-330
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• 1998

There have been several recent studies concerning the stability of fuzzy control systems and the synthesis of stabilizing fuzzy controller. This paper reports on a related study of the TS(Takagi-Sugeno) fuzzy systems, and it is shown that the controller synthesis problems for the nonlinear systems described by the TS fuzzy model can be reduced to convex problems involving LMIs(Linear matrix inequalities). After classifying the TS fuzzy systems into two families based on how diverse their input matrices are, different controller synthesis procedure is given for each of these families. A numerical example is presented to illustrate the synthesis procedures developed in this paper.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.429-432
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• 1998

This paper is to design stable fuzzy controller so as to control chaotic nonlinear systems effectively via fuzzy control system and Parallel Distributed Compensation (PDC) design. To design fuzzy control system, nonlinear systems are represented by Takagi-sugeno(TS) fuzzy models. The PDC is employed to design fuzzy controllers from the TS fuzzy models. The stability analysis and control design problems is to find a common Lyapunov function for a set of linear matrix inequalitys(LMIs). The designed fuzzy controller is applied to Rossler system. The simulation results show the effectiveness of our controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.793-800
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• 2001

This paper deals with a new design methodology for a switching-type fuzzy-model-based controller in continuous and discrete-time system. Takagi-Sugeno (TS) fuzzy model is employed to design the switching-type fuzzy-model-based controller. A switching-type fuzzy-model-based controller is constructed based on the spirit of “divide and conquer”. The global system which has several rules in divided into several subsystems and then, a solution is found at each subsystem. The global solution is determined by a conjunction of the solutions of each subsystem. The design conditions are formulated in terns of linear matrix inequalities (LMIs), which guarantee the stabilization of a given TS fuzzy system. Simulation examples are included for ensuring the proposed control method.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1854-1855
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• 2006

In this paper, the Takagi-Sugeno (TS) fuzzy state estimation scheme, which is suggested for a steady state estimator using standard Kalman filter theory with uncertainties. In that case, the steady state with uncertain can be represented by the TS fuzzy model structure, which is further rearranged to give a set of uncertain linear model using standard Kalman filter theory. And then the unknown uncertainty is regarded as an additive process noise. To optimize fuzzy system, we utilize the genetic algorithm. The steady state solutions can be found for proposed linear model then the linear combination is used to derive a global model. The proposed state estimator is demonstrated on a truck-trailer.