• Journal of Advanced Marine Engineering and Technology
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• v.21 no.4
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• pp.393-403
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• 1997

In this paper, we proposed an optimal identification method of identifying the membership func¬tions and the fuzzy rules for the stabilization controller of the nonlinear system by RVEGA( Real Variable Elitist Genetic Algo rithm l. Although fuzzy logic controllers have been successfully applied to industrial plants, most of them have been relied heavily on expert's empirical knowl¬edge. So it is very difficult to determine the linguistic state space partitions and parameters of the membership functions and to extract the control rules. Most of conventional approaches have the drastic defects of trapping to a local minima. However, the proposed RVEGA which is similiar to the processes of natural evolution can optimize simulta¬neously the fuzzy rules and the parameters of membership functions. The validity of the RVEGA - based fuzzy controller was proved through applications to the stabi¬lization problems of an inverted pendulum system with highly nonlinear dynamics. The proposed RVEGA - based fuzzy controller has a swing -. up control mode(swing - up controller) and a stabi¬lization one(stabilization controller), moves a pendulum in an initial stable equilibrium point and a cart in an arbitrary position, to an unstable equilibrium point and a center of the rail. The stabi¬lization controller is composed of a hierarchical fuzzy inference structure; that is, the lower level inference for the virtual equilibrium point and the higher level one for position control of the cart according to the firstly inferred virtual equilibrium point. The experimental apparatus was imple¬mented by a DT -- 2801 board with AID, D/A converters and a PC - 586 microprocessor.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.05a
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• pp.238-242
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• 2001

In this paper, we proposed an optimal identification method of the membership functions and the numbers of fuzzy rule base for the stabilization controller of the Thermal process control system by RVEGA. Although fuzzy logic controllers and expert systems have been successfully applied in many complex industrial process, they must rely on experts knowledges. So it is difficult in determination of the linguistic state space, definition of the membership functions of each linguistic term and the derivation of the control rules. To verify the validity of this RVEGA-based fuzzy controller, Thermal process control system, with strong nonlinear dynamics, was selected for application of this algorithm and compare with PI controller, and the empirically improved fuzzy controller.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.10
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• pp.483-494
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• 2002

In this thesis an optimal design technique of fuzzy logic controller using the real variable elitist genetic algorithm(RVEGA) as a supplementary control to Static Var Compensator(SVC) in order to damp oscillation in an AC-DC Dower system was proposed. Fuzzy logic controller is designed self-tuning shape of fuzzy rule and fuzzy variable using genetic algorithm based on natural selection and natural genetics. To verify the robustness of the proposed method, considered dynamic response of system by applying a load fluctuation.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2099-2101
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• 2001

In general, the thermal control system has nonlinearity and the time delay, futhermore, it is difficult to design the free size controller, because the external environmental disturbances, such as rapid temperature change. Many researchers in this field are preferring to adapt the fuzzy logic control methods. But it is noted that the actuator identification of M.F.'s used in FLC is very difficult. Therefore in this paper, an implementation technique of thermal control system using RVEGA based optimal fuzzy control was proposed. It's superiority and exaction in controller design processes hardware in implementation were proved through a series of simulations and experimentations.