• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.2
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• pp.68-73
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• 2000

This paper addresses the analysis and design of fuzzy control systems for a class of complex nonlinear systems. Firstly, the nonlinear system is represented by Takagi-Sugeno(TS) fuzzy model and the global controller is constructed by compensating each linear model in the rule of TS fuzzy model. The design of conventional TS fuzzy-model-based controller is composed of two processes. One is to determine the static state feedback gain of each local model and the other is to validate the stability of the designed fuzzy controller. In this paper, we propose an alternative methods for the design of TS fuzzy-model-based controller. The design scheme is based on the extension of conventional optimal control theory to the design of TS fuzzy-model-based controller. By using the proposed method, the design and stability analysis of the TS fuzzy model-based controller is reduced to the problem of finding the solution of a set of algebraic Riccati equations. And we use the recently developed interior point method to find the solution of AREs, where AREs are recast as the LMI formulation. A numerical simulation example is given to show the effectiveness and feasibiltiy of the proposed fuzzy controller design method.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.459-464
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• 2001

This paper discusses a stochastic stabilization of Takagi-Sugeno(TS) fuzzy system with Markovian input delay. The finite Markovian process is adopted to model the input delary of the overall control system. It is assumed that the zero and hold devices are used for control input. The continuous-time TS fuzzy system with the Markovian input delay is discretized for easy handling delay, according, the discretized TS fuzzy system is represented by a discrete-time TS fuzzy system with jumping parameters. The stochastic stabilizibility of the jump TS fuzzy system is derived and formulated in terms of linear matrix inequalities (LNIS)

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.260-263
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• 2000

In this paper, a tracking 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 tracking problem of TS fuzzy system is converted into the regulation problem of a multiple linear system. A sufficient condition for robust tracking 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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• 1999.07b
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• pp.528-530
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• 1999

This paper addresses the analysis and design of fuzzy control systems for a class of complex single input single output nonlinear systems. Firstly, the nonlinear system is represented by well-known Takagai-Sugeno (TS) fuzzy model and the global controller is constructed by compensating each linear model in the rule of TS fuzzy model. The design of conventional TS fuzzy-model-based controller usually is composed of two processes. One is to determine static state feedback gain of each local model and the other is to validate the stability of the designed fuzzy controller. In this paper, we propose an alternative of the design of TS fuzzy-model-based controller. The design scheme is based on the extension of conventional optimal control theory to the design of TS fuzzy-model-based controller. By using the proposed method the design and stability analysis of the TS fuzzy model-based controller is reduced to the problem of finding the solution of a set of algebraic Riccati equations. And we use the recently developed interior point method to find the solution of AREs, where AREs are recast as the LMI formulation. One simulation example is given to show the effectiveness and feasibility of the proposed fuzzy controller design method.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.191-194
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• 2000

In this paper, an output tracking control technique of discrete-time Takagi-Sugeno (TS) fuzzy systems is developed. The TS fuzzy system is represented as an uncertain multiple linear system. The tracking problem of TS fuzzy system is converted into the stabilization problem of a uncertain multiple linear system. A sufficient condition for asymptotic tracking 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 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 Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.153-156
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• 2000

In this paper, a mathematical model of a permanent-magnet synchronous motor (PMSM) is derived, and the steady-state characteristics of this system, when subject to constant input voltages and constant external torque, are formulated. It is shown that the PMSM model can exhibit a variety of chaotic phenomena, under some choices of system parameters and external inputs. Based on TS fuzzy modeling methodology, the TS fuzzy model of the PMSM chaotic system is presented, so the interaction between fuzzy system and chaos can be explored, and then fuzzy-model-based control methodologies can be used to control chaos in chaotic systems. Computer simulations show that the strange attractors in the derived TS fuzzy system and original chaotic system are topologically equivalent.

• 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 Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.111-115
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• 2001

In this paper, a simple and systematic control design method is proposed for a discrete-time Takagi-Sugeno(TS) fuzzy system, which employs the parallel distributed compensation(PDC) to determine the structure of a fuzzy controller so as to mark all the Lyaunov exponents of the controlled TS fuzzy system strictly positive. This approach is proven to be mathematically rigorous for anticontrol of chaos for a TS fuzzy system in the sense that any given discrete-time TS fuzzy system can be made chaotic by the designed PDC controller along with the-operation. A numerical example is included to visualize the anticontrol effect.