• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.400-403
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• 2001

In this paper, the classical Lyapunov synthesis method for designing controllers is extended to fuzzy logic. This control technique is then applied to the design of a novel tracking controller for reluctance motor drives. The main features of the method are small rule base, simplicity of construction, and low cost. The proposed controller has been simulated for a model case. In addition, its dynamic performances have been shown to be satisfactory. Capabilities of the proposed technique in controlling the highly nonlinear systems of reluctance motors with much simplicity are also verified.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.672-680
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• 2012

This paper proposes the control strategy of the shunt Flexible AC Transmission System (FACTS) devices to improve transient stability in multimachine power system. The multimachine power system has high nonlinear response after severe disturbance. The concept of Lyapunov energy function is applied to derive nonlinear control strategy and it was found that the time derivative of line voltage is not only can apply to control the shunt FACTS devices in multimachine system but also is locally measurable signal. The fuzzy logic control is also applied to overcome the uncertainty of various disturbances in multimachine power system. This paper presents the method of investigating the effect of the shunt FACTS devices on transient stability improvement. The proposed control strategy and the method of simulation are tested on the new England power system. It was found that the shunt FACTS devices based on the proposed nonlinear control strategy can improve transient stability of multimachine power system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.127-137
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• 2006

This paper presents an $H_{\infty}$ controller synthesis method for discrete time fuzzy dynamic systems based on a piecewise smooth Lyapunov function. The basic idea of the proposed approach is to construct controllers for the fuzzy dynamic systems in such a way that a Piecewise smooth Lyapunov function can be used to establish the global stability with $H_{\infty}$ performance of the resulting closed loop fuzzy control systems. It is shown that the control laws can be obtained by solving a set of Bilinear Matrix Inequalities (BMIs). An example is given to illustrate the application of the proposed method.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.658-662
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• 2004

This paper develops a stability analysis and controller synthesis methodology for a continuous-time affine Takagi-Sugeno (T-S) fuzzy systems with parametric uncertainties. Affine T-S fuzzy system can be an advantage because it may be able to approximate nonlinear functions to high accuracy with fewer rules than the homogeneous T-S fuzzy systems with linear consequents only. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The search for a piecewise quadratic Lyapunov function can be represented in terms of bilinear matrix inequalities (BMIs). A simulation example is given to illustrate the application of the proposed method.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.226-228
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• 2006

This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems are used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system, we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem, and convergence and robustness properties are demonstrated. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.765-768
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• 1993

Adaptive Control is used in order to improve close loop dynamics with a fuzzy controller when process parameters are unknown or fluctuate form an initial value. The way in which the adaptive control environment may be applied is the following. First we obtain a linear fuzzy controller. Second, we apply the adaptive rules by means of actuating directly over fuzzy variables which change their value. The techniques are based on Lyapunov functions. Third, we comment about extending this method to non-piecewise linear controllers using the contrast definition for a fuzzy controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.302-306
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• 2011

This paper presents Takagi-Sugeno (T-S) fuzzy-model-based controller for depth control of autonomous underwater vehicles(AUVs). Through sector nonlinearity methodology, The nonlinear AUV is represented by T-S fuzzy model. By using the Lyapunov function, the design condition of controller is derived to guarantee the performance of depth control in the format of linear matrix inequality (LMI). An example is provided to illustrate the effectiveness of the proposed methodology.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2254-2259
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• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.