• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1267-1271
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• 2003

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed loop system is guaranteed. The computer simulation results for an inverted pendulum system show the performance of the proposed robust adaptive fuzzy controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.151-157
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• 2008

A non-fragile guaranteed cost control (GCC) problem is presented for a class of discrete time-delay nonlinear systems described by Takagi-Sugeno (T-S) fuzzy model. The systems are assumed to have norm-bounded time-varying uncertainties in the matrices of state, delayed state and control gains. Sufficient conditions are first obtained which guarantee that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound. Then the design method of the non-fragile guaranteed cost controller is formulated in terms of the linear matrix inequality (LMI) approach. A numerical example is given to illustrate the effectiveness of the proposed design method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.198-198
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• 2000

In this paper, the linearization method of nonlinear systems via fuzzy controllers which can be the basic research for designing a systematic fuzzy controllers with singleton consequents is proposed. We regard a closed loop system as a canonical form and propose a methodology for linearization of nonlinear system which adjust the input at vertex using zero condition and affinity condition. Through some examples, we show the validity of the proposed method and it can be extended to the design problem of fuzzy controller.

• Earthquakes and Structures
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• v.23 no.4
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• pp.329-338
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• 2022

This work addresses the optimization controller design problem combining the AI evolution bat (EB) optimization algorithm with a fuzzy controller in the practical application of a reinforced concrete frame structure. This article explores the use of an intelligent EB strategy to reduce the dynamic response of Lead Rubber Bearing (LRB) composite reinforced concrete frame structures. Recently developed control units for plant structures, such as hybrid systems and semi-active systems, have inherently non-linear properties. Therefore, it is necessary to develop non-linear control methods. Based on the relaxation method, the nonlinear structural system can be stabilized by properly adjusting the parameters. Therefore, the behavior of a closed-loop system can be accurately predicted by determining the behavior of a closed-loop system. The performance and durability of the proposed control method are demonstrated by numerical simulations. The simulation results show that the proposed method is a viable and feasible control strategy for seismically tuned composite reinforced concrete frame structures.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.451-456
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• 2015

In this paper, an observer-based decentralized fuzzy controller is designed for discrete-time interconnected fuzzy systems. Based on the fuzzy subsystem of the interconnected fuzzy system, the observer-based decentralized fuzzy controller is considered. By using the fuzzy subsystem and the observer-based decentralized fuzzy controller, the closed-loop system is obtained. From the closed-loop system, the stability condition with the maximum interconnection bound is developed, and its sufficient condition is represented as the linear matrix inequality (LMI). Finally, the numerical example is provided to verify the effectiveness of the proposed technique.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.83.4-83
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• 2002

$\bullet$ We present a state-feedback RHC for discrete-time TS fuzzy systems with input constriants. $\bullet$ The controller employ the current and one-step past information on the fuzzy weighting functions. $\bullet$ It is obtained from the finite horizon optimization problem with the invariant ellipsoid constraint $\bullet$ Under parameterized LMI conditions on the terminal weighting matrix $\bullet$ The closed-loop system stability is guaranteed. $\bullet$ The parameterized linear matrix inequalities are relaxed to a finite number of solvable LMIs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.225-231
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• 1998

TSK fuzzy system can represent effectively the behavior of a complex nonlinear system with low number of rules with the desired accuracy and guarantee the stability of the closed loop system, while the interpretation of the rules is difficult due to the functional nature of the consequents. On the contrary, fuzzy controller with singleton consequents is understandable intuitively and adjustable the rules easily due to qualitative expression of the rules. Ideally, one would like to combine the positive identification properties of TSK fuzzy system with the advantages of fuzzy controller with singleton consequents. Therefore, this paper suggests a method transforming TSK fuzzy systems into fuzzy systems with singleton consequents, and shows its application designing a fuzzy controller with singleton consequents by using the TSK fuzzy system when the behavior of a nonlinear system is described with a singleton fuzzy model by human esper.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.676-680
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• 1991

This paper proposes fuzzy control algorithms for a DC moter speed control. The proposed algorithms are constructed by the fuzzy controller and the fuzzy compensator. The fuzzy compensator used to overcome rapidly the effects caused by the disturbance and is mounted outside of the closed loop of the fuzzy controller. The fuzzy control rules are established from human operator experience and basic engineering knowledge about the process dynamics. Simulation results show that the proposed algorithms compensate for parameter variation and disturbance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.156-165
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• 1998

This paper proposes a systematic design methodology for the Takagi-Sugeno(TS) model based fuzzy control system with guaranteed stability and additional constraints on the closed-loop pole location. These combined two objectives are formulated as a system of LMIs(Linear Matrix Inequalities). Since LMIs intrinsically reflect constraints, they tend to offer more flexibility for combining various constraints on the closed-loop system. To demonstrate the usefulness of the proposed design methodology it is applied to the requlation problem of a nonlinear magnetic bearing system. Simulation results show that the proposed LMI-based design methodology yields not only maximized stability boundary but also the desired transient responses.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2441-2443
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• 2003

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed loop system is guaranteed. The computer simulation results for an inverted pendulum system show the performance of the proposed robust adaptive fuzzy controller.