• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1516-1520
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• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control a nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.1
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• pp.162-170
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• 2001

This paper presents a scheme for designing a fuzzy model-bsaed state observer for nonlinear system. For this scheme, a Tagaki-Sugeno type fuzzy model whose consequent part is of the state space form is obtained. In describes the locally linear input/output relationship of a system. The parameters of the fuzzy model are adjusted using a genetic algorithm. Then. fuzzy full-order and reduced-order state observers are designed based on the fuzzy model. A set of simulation works is carried out to demonstrate the effectiveness of the proposed scheme.

• 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.17 no.2
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• pp.276-283
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• 2007

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.68-75
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• 2004

In this paper, an estimator with an appropriate adaptive law for updating parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the parameterized plant model. Using the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for indirect adaptive fuzzy control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1250-1253
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• 1993

The fuzzy control theory is applied to control a container crane, which is a very complicated system and controled manually by experts. As reference velocities of trolley and hoist of the container crane, we use those decided by experts, and express them by fuzzy model. We control the crane to follow the reference velocities by using fuzzy controllers. The fuzzy controllers are designed on the container crane. We made a model container crane and applied the suggested method to it

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.235-242
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• 2003

In this paper, a fuzzy controller is designed to suppress and stabilize the chaotic behavior of Chua's circuit. This controller is constructed by the following two phases. First, Chua's circuit is represented by an affine fuzzy model. Second, a fuzzy controller is designed so that the stability of the closed-loop system composed of the fuzzy controller and the affine fuzzy model of Chua's circuit is rigorously guaranteed. The stability condition of the affine fuzzy system is derived and is recast in the formulation of linear matrix inequalities. The guaranteed stability is global and asymptotic. Finally, the applicability of the suggested methodology is highlighted via computer simulations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.145-150
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• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.303-311
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• 2012

In this study, we introduce a dynamic process model as well as the design methodology of optimized fuzzy controller for its efficient application to vacuum production system to produce a semiconductor, solar module and display and so on. In a vacuum control field, PID control method is widely used from the viewpoint of simple structure and preferred performance. But, PID control method is very sensitive to the change of environment of control system as well as the change of control parameters. Therefore, it's difficult to get a preferred performance results from target system which has a complicated structure and lots of nonlinear factors. To solve such problem, we propose the design methodology of an optimized fuzzy PID controller through a following series of steps. First a dynamic characteristic of the target system is analyzed through a series of experiments. Second the process model is built up and its characteristic is compared with real process. Third, the optimized fuzzy PID controller is designed using genetic algorithms. Finally, the fuzzy controller is applied to target system and then its performance is compared with that of other conventional controllers(PID, PI, and Fuzzy PI controller). The performance of the proposed fuzzy controller is evaluated in terms of auto-tuned control parameters and output responses considered by ITAE index, overshoot, rise time and steady state time.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.3
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• pp.21-31
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• 2007

This study is aimed at the development of a runoff forecasting model by using the Fuzzy inference system and Neural Network model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting. The Neuro-Fuzzy (NF) model were used in this study. The NF model, recently received a great deal of attention, improve the existing Neural Networks by the aid of the Fuzzy theory applied to each node. The study area is the downstreams of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model respectively. The schematic diagram method and the statistical analysis are conducted to evaluate the feasibility of rainfall-runoff modeling. The model accuracy was rapidly decreased as the forecasting time became longer. The NF model can give accurate runoff forecasts up to 4 hours ahead in standard above the Determination coefficient $(R^2)$ 0.7. In the comparison of the runoff forecasting using the NF and TANK models, characteristics of peak runoff in the TANK model was higher than ones in the NF models, but peak values of hydrograph in the NF models were similar.