• 대한전기학회논문지:시스템및제어부문D
• /
• 제53권2호
• /
• pp.65-75
• /
• 2004

This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
• /
• pp.23-25
• /
• 1993

This paper presents a design technique of self-organizing fuzzy controller using a learning method of fuzzy inference rule by a gradient method for load frequency control of power system. The membership functions in antecedent part and in consequent part of fuzzy inference rules are tuned by the gradient method. The related simulation results show that the proposed fuzzy controller are more powerful than the conventional ones for reduction of undershoot and deviation of load frequency in steady-state, and for minimization of settling time.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
• /
• pp.126-129
• /
• 1997

This paper propose a new hybrid genetic algorithm for auto-tunig auzzy controller improving the performance. In general, fuzzy controller used pre-determine d moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controller, using hybrid genetic algorithms. The object of the proposed algorithm is to promote search efficiency by overcoming a premature convergence of genetic algorithms. Hybrid genetic algorithm is based on genetic algorithm and modified gradient method. Simulation results verify the validity of the presented method.

• Journal of Electrical Engineering and Technology
• /
• 제4권1호
• /
• pp.66-78
• /
• 2009

This paper introduces the design of a fuzzy logic controller in conjunction with direct torque control strategy for a Permanent Magnet synchronous machine. A stator flux angle mapping technique is proposed to reduce significantly the size of the rule base to a great extent so that the fuzzy reasoning speed increases. Also, a fuzzy resistance estimator is developed to estimate the change in the stator resistance. The change in the steady state value of stator current for a constant torque and flux reference is used to change the value of stator resistance used by the controller to match the machine resistance.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
• /
• pp.863-868
• /
• 1992

This paper presents a method on applying Genetic Algorithm(GA), which is a well-known high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilizes Sugeno's hybrid inference method, which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the optimal parameters in the FLC. The proposed approach will be demonstrated using 2 d.o.f robot manipulator to verify its effectiveness.

• 한국지능시스템학회논문지
• /
• 제2권3호
• /
• pp.58-67
• /
• 1992

This paper presents a method on applying Genetric Algorithms(GA), which is a well-know high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilized Sugeno's hybrid inference method. which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the iptimal parameters in the FLC. The proposed approach will be demonstrated using 2 d. o. f robot manipulator to verify its effectiveness.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2009년도 제40회 하계학술대회
• /
• pp.778_779
• /
• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.

• 조명전기설비학회논문지
• /
• 제20권4호
• /
• pp.98-106
• /
• 2006

본 논문은 동손과 철손을 최소화 하는 SynRM을 위하여 효율 최적화 알고리즘을 제시하였다. 속도 제어기의 설계는 퍼지제어와 신경회로망을 사용한 적응 퍼지-신경회로망(AFNN) 제어기에 기반을 두었다. 특정 토크는 d와 q축의 전류성분의 다양한 합성으로 얻을 수 있다. 효율최적화 제어는 정상상태에 일정한 동작점에서 최소 손실을 제공하는 d와 q축 전류 성분의 조합을 찾는다. 직접 조절되는 토크 발생은 효율최적화 제어 설계에 의하여 매우 잘 조절되어지는 전류 성분이다. 제시된 알고리즘은 속도변화와 양호한 토크제어를 위해 전자기적 손실을 허용한다. HAI 제어기의 제어성능은 다양한 동작상태 분석을 통하여 평가한다. 분석된 결과는 제시된 알고리즘의 타당성을 보여준다.

• 조명전기설비학회논문지
• /
• 제21권5호
• /
• pp.1-9
• /
• 2007

본 논문은 SynRM의 동손 및 철손을 최소화 하는 효율 최적화 제어를 제시한다. 퍼지와 신경회로망으로 구성된 적응 퍼지-신경회로망 제어기를 바탕으로 한 속도 제어기를 설계한다. 특정 전동기 토크를 발생하는 d-q축 전류 조합은 무수히 많이 존재한다. 효율 최적화 제어기의 목적은 정상상태에 확실한 동작점에서 d-q축 전류 조합을 찾는 것이다. 제시된 알고리즘은 양호한 동적 토크 제어를 유지하는 동안 속도 및 토크 변화를 감소시키기 위하여 전자기적 손실은 허용한다. HAI 제어기의 제어 성능은 다양한 동작 상태에서 평가된다. 결과 분석은 제시된 알고리즘의 타당성을 보여준다.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제53권11호
• /
• pp.753-759
• /
• 2004

This paper presents a modeling and simulation of a fuzzy controller for maximum power extraction of a grid-connected wind energy conversion system with a link of a rectifier and an inverter. It discusses the maximum power control algorithm for a wind turbine and proposes, in a graphical form, the relationships of wind turbine output, rotor speed, power coefficient, tip-speed ratio with wind speed when the wind turbine is operated under the maximum power control. The control objective is to always extract maximum power from wind and transfer the power to the utility by controlling both the pitch angle of the wind turbine blades and the inverter firing angle. Pitch control method is mechanically complicated, but the control performance is better than that of the stall regulation method. The simulation results performed on MATLAB will show the variation of generator's rotor angle and rotor speed, pitch angle, and generator output.