• Proceedings of the KIEE Conference
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• 2004.10a
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• pp.182-184
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• 2004

This paper presents a PI controller tuning method for high performance permanent magnet synchronous motor (PMSM) drives under load variations using clustering fuzzy algorithm. In many speed tracking control systems PI controller has been used due to its simple structure and easy of design. PI controller, however, suffers from the electrical machine parameter variations and disturbances. In order to improve the tracking control performance under load variations, the PI controller parameters are modified during operation by clustering fuzzy method. This method based on optimal fuzzy logic system has simple structure and computational simplicity. It needs only sample data which is obtained by optimal controller off-line. As the sample data implemented in the adaptive fuzzy system can be modified or extended, a flexible control system can be obtained Simulation results show the usefulness of the proposed controller.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.530-532
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• 1999

In adaptive fuzzy control, system designer develops an adaptive law for the output of the unknown plant to track a given signal. The adaptation gains of the adaptive law are critical elements in the overall system, however, they were used to be selected by the designer's experience or intuition. In this paper, genetic algorithm is used to search an optimal adaptation gain and simulation results will be presented to show the improved tracking responses.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.2
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• pp.69-76
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• 2014

This paper proposes a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of Satellite, Information and Communications
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• v.8 no.3
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• pp.6-9
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• 2013

Cognitive Radio is an intelligent interference avoidance idle spectrum communication system for your environment and actively determine the frequency bandwidth recycling by sharing the spectrum in a way that maximizes the efficiency of radio resource technology. At this time, this does not cause interference to the PU spectrum sensing technology is important. If you can choose, depending on the state of the channel spectrum sensing algorithm will be more efficient sensing. Matched filter detection system model that reflects the individual sensing through a fuzzy controller in this paper, energy detection and self-correlation detection are proposed and analyzed.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.521-523
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• 1999

In this paper, we propose the hybrid fuzzy controller(HFC) and the hybrid auto-tuning algorithm. The proposed HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance such as sensitivity improvement in steady state and robustness in transient state than any other controller. In addition, a hybrid auto-tuning algorithm which consists of genetic algorithm and complex algorithm to automatically generate weighting factor, scaling factors and PID control gains optimizes the output of HFC. As an typical example of non-linear system in control theory an inverted pendulum will be controlled by the suggested HFC and illustrated the performance and applicability of this proposed method by simulation.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.588-590
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• 1999

In this paper the design method of a fuzzy logic controller with a genetic algorithm is proposed. Fuzzy logic controller is based on linguistic descriptions(in the form of fuzzy IF-THEN rules) from human experts. The auto-tuning method is presented to automatically improve the output performance of controller utilizing the genetic algorithm. The GA algorithm estimates automatically the optimal values of scaling factors and membership function parameters of fuzzy control rules. Controllers are applied to the processes with time-delay and the DC servo motor. Computer simulations are conducted at the step input and the output performances are evaluated in the ITAE.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.5
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• pp.205-211
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• 2000

This paper presents an optimal design method for fuzzy logic controller (FLC) of HVDC using an Adaptive Evolutionary Algorithm(AEA). We have proposed the AEA which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary algorithms. The AEA is used for tuning fuzzy membership functions and scaling constants. Simulation results show that disturbances are well damped and the dynamic performances of FLC have better responses than those of PD controller when AC system load changes suddenly.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.416-419
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• 2001

In this paper a new approach to estimate scaling factors of fuzzy controllers such as the fuzzy PID controller and the fuzzy PD controller is presented. The performance of the fuzzy controller is sensitive to the variety of scaling factors[1]. The desist procedure dwells on the use of evolutionary computing(a genetic algorithm) and estimation algorithm for dynamic systems (the inverted pendulum). The tuning of the scaling factors of the fuzzy controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy controller by means of two types of estimation algorithms such as Neuro-Fuzzy model, and regression polynomial [7]. This method can be applied to the nonlinear system as the inverted pendulum. Numerical studies are presented and a detailed comparative analysis is also included.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.897-899
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• 1999

This paper presents a scaling factor tuning method to improve the performance of fuzzy logic controller. Tuning rules and reasoning are utilized off-line to determine the scaling factors based on absolute value of the error and its difference. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a dc-servo motor control system. The performance of this control system is demonstrated higher than a conventional fuzzy logic controller(FLC).

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1073-1074
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• 2008

This paper presents outdoor reset control based on fuzzy algorithm for radiant floor heating system. We construct fuzzy system under indoor temperature and outdoor temperature. Simulation is based on TRNSYS with MATLAB. MATLAB is calculating and decide heat source using fuzzy system. Energy efficiency of Fuzzy algorithm is analyzed in term of indoor by TRNSYS System.