• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.917-920
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• 1993

Various forms of hardware alternatives exist for the implementation of fuzzy logic controllers. In this paper, we describe a systematic framework for realizing fuzzy heuristics on programmable-logic-devices. Our approach is suitable for the automated development of fuzzy logic controllers.

• 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

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1094-1097
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• 1993

In this paper, the absolute stability criterion of nonlinear plants with sector bounded nonlinear feedback is derived. The result obtained is useful for applications, in particular, stability analysis and design of fuzzy logic controllers.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.48-57
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• 1997

This paper presents the automatic construction and parameter optimization technique for fuzzy logic controllers using genetic algorithm. In general. the design of fuzzy logic controllers has difficulties in the acq~lisition of expert's knowledge and relies to a great extent on empirical and heuristic knowledge which, in many cases, cannot be objectively justified. So, the performance of the controllers c:an be degraded in the case of plant parameter variations or unpredictable incident which a designer may have ignored, and the parameters of fuzzy logic controllers obtained by expert's control action may not be optirnal. Some of these problems can be resolved by the use of genetic algorithm. The proposed method can tune the parameters of fuzzy logic controllers including scaling factors and determine: the appropriate number of fuzzy rulcs systematically. Finally, we provides the second order dead time plant to evaluate the feasibility and generality of the proposed method. Comparison shows that the proposed method can produce fuzzy logic controllers with higher accuracy and a smaller number of fuzzy rules than manually tuned fuzzy logic controllers.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.102-109
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• 2014

In this paper, an algorithm to design fuzzy PID controllers is proposed. The proposed controllers are composed of fuzzy rules of which consequences are linear PID controllers and are designed with help of TSK fuzzy controllers. TSK fuzzy controllers are designed from TSK fuzzy model using pole assignment and have outstanding ability making the output response of nonlinear systems similar to the desired one. However, because of its structure complexity the TSK fuzzy controller is difficult to be used in industry. The proposed controllers have PID controller structure which can be easily realized, and are designed by using the data obtained from control simulations with TSK fuzzy controllers. To verify the proposed algorithm, two example simulations are performed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.117-120
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• 2001

This paper presents a new global state-matching intelligent digital redesign method for nonlinear systems by using genetic algorithms (GAs). The proposed method results in global matching of the states of the analogously controlled system with those of the digitally controlled system while the conventional intelligent digital redesign method does not. The proposed method provides a new approach for the digital redesign of a class of fuzzy-model-based controllers.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.154-161
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• 2012

This article presents experimental studies of fuzzy logic application to control a two-wheel mobile robot(TWMR) system. The TWMR system is composed of two systems, an inverted pendulum system and a mobile robot system. Although linear controllers can stabilize the TWMR, fuzzy controllers are expected to have robustness to uncertainties so that the resulting performances are expected to be better. Nominal fuzzy rules are used to control balance and position of TWMR. Fuzzy logic is embedded on a DSP chip to control the TWMR. Balancing performances of the PID controller and the fuzzy controller under disturbances are compared through extensive experimental studies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.646-651
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• 1998

Fuzzy control has been used successfully in many practical applications. In traditional methods, experience and control knowledge of human experts are needed to design fuzzy controllers. However, it takes much time and cost. In this paper, an automatic design method for fuzzy controllers using genetic algorithms is proposed. In the method, we proposed an effective encoding scheme and new genetic operators. The maximum number of linguistic terms is restricted to reduce the number of combinatorial fuzzy rules in the research space. The proposed genetic operators maintain the correspondency between membership functions and control rules. The proposed method is applied to a cart centering problem. The result of the experiment has been satisfactory compared with other design methods using genetic algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.3
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• pp.56-67
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• 1993

A Class of fuzzy controller based on the variable structure system(VSS) technique in which different structures of controllers are fuzzily switched according to the switching rules in proppsed. The structure of proposed controllers was motivated by the characteristics of position type fuzzy controller and velocity type fuzzy controller ; the former generally gives good performance in transient perod and the latter are capable of reducing steady state error of response. To show the usefulness of the proposed controller, it is applied to several systems that is difficult to stabilize or difficult to get satisfactory responsed by conventional fuzzy controllers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.123-128
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• 1998

This paper proposes design of LFT-based fuzzy controllers for model-following, which are better than the previous input-output linearization controllers, which are not able to follow the model system states and which do not guarantee the stability of all states. The method proposed in this paper provides a LFT-based Takagi-Sugeno(T-S) fuzzy controller with guaranteed stability and model-following via the following steps: First, using LFT(Linear Fractional Transformation) and T-S fuzzy model, controllers, are obtained. Next, error dynamics are obtained for model-following, and errors go to 0(zero). Finally, a T-s fuzzy controller that can stabilizxe the system with the requirement on the control input satisfied is obtained by solving the LMIs with the MATLAB LMI Control Toolbox and a model-following controller is obtained. Simulations are performed for the LFT-based T-S fuzzy controller designed by the proposed method, which show better performance than the results of input-out ut linearization controller.