• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.352-354
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• 2004

In this paper, a parallel fuzzy controller for one degree of freedom magnetic levitation is designed and its performance is compared with the performance of a PID controller. Input, output scaling factor of fuzzy controller and gain of PID controller were tuned using the GA algorithm. The designed controllers are validated by numerical simulations. So it's shown that parallel fuzzy controller can give the better performance for the plant than PID controller.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1996.10a
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• pp.160-165
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• 1996

Nonlinear Attitude Dynamic Equation for fleable-body satellite is drived and confirmed the effect of flexible body. GA based Fuzzy Logic Controller is designed. Also, Bang-bang controller is designed for compare the performance, Fuzzy controller chows much batter result then those by using of Bang-Bang controller.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.93-99
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• 2009

The fuzzy control, neural network and genetic algorithm(GA) are algorithms to make the intelligence of system more higher. In this paper, we optimized the fuzzy controller using a genetic algorithm for desire response. Also a compensated fuzzy controller has dual rules. One control rule used to decrease the overshoot and rise time occurring in transient response region and another fuzzy control rule use to decrease the steady state error and rapildy to converge at the convergence region. GA is necessary to optimal the exchange time of the two fuzzy control rule base. Fuzzy-GA controller have a process of reproduction, crossover and mutation and we experimented by hydraulic servo motor control system We could observe that compensated Fuzzy-GA controller have good control performance compare to the fuzzy control technique have two rule base table.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.413-417
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• 2004

This paper proposes a fuzzy proportional-Integral (PI) controller for the precise tracking control of optical disk systems based on the genetic algorithm (GA). The fuzzy PI control rules are optimized by the GA to yield an optimal fuzzy PI controller. We validate the feasibility of the proposed method through a numerical simulation.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.2
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• pp.225-235
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• 2005

In this Paper, it was designed the GA-fuzzy controller of a Thyristor Controlled Series Capacitor(TCSC) for enhancement of power system stability. The newly designed controller of TCSC was designed to overcome the nonlinearity such as operating point change of power system as well as to respond to disturbances as uncertainties of line parameters and line fault. So, fuzzy controller by intelligent control theory was used for it. And the fuzzy controller was optimized from a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller namely. scaling factor. membership function and control rules. Nonlinear simulation results show that the proposed control technique is superior to conventional PSS in dynamic responses over the wide range of operating conditions and is convinced robustness and reliableness in view of structure.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2000.05a
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• pp.16-21
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• 2000

In this paper we design a GA-fuzzy controller for position control and anti-swing at the destination point. Applied genetic algorithm is used to complement the demerit such as the difficulty of the component selection of fuzzy controller namely scaling factor membership function and control rules. lagrange equation is used to represent the motion equation of trolley and load in order to obtain mathematical modelling. Simulation results show that the proposed control technique is superior to a conventional optimal control in destination point moving and modification.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.1
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• pp.35-42
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• 1998

This paper presents the introduction of Pseudo-2 axes robot module and its GA-based fuzzy control implementation. Pseudo-2 axes robot module which use a single motor and controller for driving 2 joints of a robot mechanism, is devised towards a lower priced robot with its degree of freedom maintained GA-based Fuzzy controller is considered for the better control implementation of the developed system than the conventional PID controller. Here. the scaling factors of the membership function with high fitness values are selected using a genetic algorithm for a pulse-type input trajectory. The obtained controller also shows better trajectory tracking performance than a PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.438-441
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• 1997

This paper studies a semi-active suspension with ER damper controlled Fuzzy Net Controller designed GA(Genetic Algorithm). Apparent viscosity of ERF(Electro-Rheological Fluid) can be changed rapidly by applying electric field. Semi-active suspension for ground vehicles are expected to improve ride quality with less vibration. This paper deals with a two-degree -of-freedom suspension using the ER damper for a quarter vehicle system. In this paper, the GA is applied for generating Fuzzy Net Controllers. The GA designs the optimal structure and performance of Fuzzy Net Controller having hybrid structure. Computer simulation results show that the semi-active suspension with ER damper has good performances of ride quality.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.99-104
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• 2002

In this paper, we construct a hybrid intelligent controller based on a fusion scheme of GA(Genetic Algorithm) and FCM(Fuzzy C-Means) clustering-based ANFIS(Adaptive Neuro-Fuzzy Inference System). In the structure identification, a set of fuzzy rules are generated for a given criterion by FCM clustering algorithm. In the parameter identification, premise parameters are optimally searched by adaptive GA. On the other hand, consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. Finally, we applied the proposed method to the truck backer-upper control and obtained a better performance than previous works.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.3-12
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• 1995

A controller utilizing fuzzy logic is developed to control the speed of a motor in a washing machine by choosing an appropriate phase. Due to the hardship imposed on obtaining a result from a relation established for inputs, present speed and present rate of speed, and ouput, a phase, of the system that can be tested against an experimental result, it is impossible to apply a genetic algorithm to fine-tune the fuzzy logic controller. To avoid this difficulty, a proper assumption that the parameters of an if-part of a primary fuzzy logic controller have a functional relationship with an error between computed values and experimental ones in made. Setting up of a fuzzy relationship between the parameters and the errors is then achieved through experimentally obtained data. Genetic Algorithm is then applied to this secondary fuzzy logic controller to verify the fuzzy logic. In the verification process, the primary fuzzy logic controller is used in obtaining experimental results. In this way the kind of difficulty in obtaining enough experimental values used to verify the fuzzy logic with genetic algorithm is gotten around. Selection of the parameters that would produce the least error when using the secondary fuzzy logic controller is done with applying genetic algorithm to the then-part of the controller. In doing so the optimal values for the parameters of the if-part of the primary fuzzy logic controller are assumed to be contained. The experimental result presented in the paper validates the assumption.