• 전력전자학회논문지
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• 제18권4호
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• pp.349-358
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• 2013

In this paper, the sensorless MPPT algorithm is proposed where the performance of varied duty ratio change has been improved using multi-layer neuro-fuzzy that aligns with neuro-fuzzy based optimized membership function. Since the change of duty ratio of sensorless MPPT is varied by using the neuro-fuzzy, the MPPT response speed is faster than the convectional method and is able to reduce the steady-state ripple. The neuro fuzzy controller has the response characteristics which is superior to the existing fuzzy controller, because of the usage of the optimal width of the fuzzy membership function. The effectiveness of the proposed method has been verified by simulations and experimental results.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.390-393
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• 2007

In this paper, applications of self recurrent neuro-fuzzy controller to stabilization of nonlinear system are considered. The architecture of self recurrent neuro-fuzzy controller is fix layer, and the hidden layer is comprised of self recurrent architecture. Also, generalized dynamic error-backpropagation algorithm is used for the learning of the self recurrent neuro-fuzzy controller. To demonstrate the efficiency of the self recurrent neuro-fuzzy control algorithm presented in this study, a self recurrent neuro-fuzzy controller was designed and then a comparative analysis was made with LQR controller through an simulation.

• 대한전자공학회논문지TE
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• 제39권4호
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• pp.414-422
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• 2002

본 논문에서는 적응 제어 문제를 다루기 위해 CFCM 클러스터링과 퍼지 균등화 기법을 이용하여 새로운 적응 뉴로-퍼지 제어기를 설계하고자 한다. 먼저 오프라인에서 CFCM은 입력데이터의 성질과 출력 패턴의 성질까지도 고려한 퍼지 클러스터링 기법으로 적응 뉴로-퍼지 제어기의 구조동정을 수행한다. 파라미터 동정은 역전과 알고리즘과 RLSE(Recursive Least Square Estimate)을 이용한 하이브리드 학습을 수행한다. 온라인 학습에서는 시변특성으로 인해 전제부 및 결론부 파라미터를 실시간으로 계산된다. 시뮬레이션으로 온 라인 적응 뉴로-퍼지 제어 시스템의 성능을 입증하기 위해 목욕물 온도제어 시스템에 대해 다루고 전형적인 퍼지 제어기에 비해 오프 라인과 온 라인 설계 모두 좋은 성능을 보이고자 한다.

• 전기학회논문지P
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• 제56권3호
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• pp.129-135
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• 2007

This paper deals with speed-sensorless control of DC servo motor using Neuro-Fuzzy Observer. DC servo motor has very low rotor inertia and excellent response characteristic and it is very useful to control torque and speed. It is easy to detect the voltage and current and resolver or encoder is used to measure a rotor speed. But it has a limit as a driving speed to detect speed precisely. So it is problem to improve the performance of the driving system. To solve this problem, it is studied to detect a speed of DC servo motor without sensor. In particular, study on the method to estimate the speed using the observer is performed a lot. In this paper, the gain of the observer is properly set up using the Neuro-Fuzzy control and Neuro-Fuzzy Observer that have a superior transient characteristic and is easy to implement compared the existing method is designed. It calculates the differentiation of the rotor current directly using the rotor current measured in the DC servo motor and estimates the speed of the rotor using the differentiation. Proposed speed sensorless control method is performed using the estimated speed. Also, it is proved feasibility of the proposed observer from the comparison tested a case with a speed sensor and a case without a speed sensor which used a highly efficient drive and 200[w] DC servo motor starting system.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권4호
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• pp.158-160
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• 2006

In this paper, a neuro-fuzzy observer system is designed using neuro-fuzzy system for speed control of AC servo motor. This neuro-fuzzy observer is proposed to with the problems occur in the Luenberger observer and sliding observer. The problems of Luenberger and sliding observer are to have to know the dynamics and internal parameters of the system. Performance of the neuro-fuzzy observer system has verified through the experiment with dynamometer load. It is shown that feasibility of the neuro-fuzzy observer is verified.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.443-448
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• 1998

As the strip caster system that produces a regular steel plate can be considered as a complicate nonlinear multi-variable system, it is not easy to obtain an effective control system. One way to overcome the difficulties is to apply the intelligent neuro-fuzzy fusion approach in developing the control scheme. The neuro-fuzzy control scheme possesses several distinct advantages, including the fact that it doesn't need the exact mathematical modeling of controlled plant and can provided some robustness in the control scheme. In this paper, an intelligent neuro-fuzzy for the stripe caster system will be proposed. The effectiveness of the proposed scheme will be demonstrated by computer simulation.

• 대한전기학회논문지:전력기술부문A
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• 제49권3호
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• pp.95-106
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• 2000

The load frequency control of power system is one of important subjects in view of system operation and control. That is even though the rapid load disturbances were applied to the given power system, the stable and reliable power should be supplied to the users, converging unconditionally and rapidly the frequency deviations and the tie-line power flow one on each area into allowable boundary limits. Nonetheless of such needs, if the internal parameter perturbation and the sudden load variation were given, the unstable phenomenal of power system can be often brought out because of the large frequency deviation and the unsuppressible power line one. Therefore, it is desirable to design the robust neuro-fuzzy controller which can stabilize effectively the given power system as soon as possible. In this paper the robust neuro-fuzzy controller was proposed and applied to control of load frequency over multi-area power system. The architecture and algorithm of a designed NFC(Neuro-Fuzzy Controller) were consist of fuzzy controller and neural network for auto tuning of fuzzy controller. The adaptively learned antecedent and consequent parameters of membership functions in fuzzy controller were acquired from the steepest gradient method for error-back propagation algorithm. The performances of the resultant NFC, that is, the steady-state deviations of frequency and tie-line power flow and the related dynamics, were investigated and analyzed in detail by being applied to the load frequency control of multi-area power system, when the perturbations of predetermined internal parameters. Through the simulation results tried variously in this paper for disturbances of internal parameters and external stepwise load stepwise load changes, the superiorities of the proposed NFC in robustness and adaptive rapidity to the conventional controllers were proved.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.203-208
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• 1998

This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

• Smart Structures and Systems
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• 제15권4호
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• pp.931-947
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• 2015

This paper focuses on the design of an intelligent control system. The used techniques are based on Neuro Fuzzy approaches applied to a magnetorheological damper in order to reduce the vibrations over footbridges; it has been applied to the Science Museum Footbridge of Valladolid, particularly. A model of the footbridge and of the damper has been built using different simulation tools, and a successful comparison with the real footbridge and the real damper has been carried out. This simulated model has allowed the reproduction of the behaviour of the footbridge and damper when a pedestrian walks across the footbridge. Once it is determined that the simulation results are similar to real data, the control system is introduced into the model. In this sense, different strategies based on Neuro Fuzzy systems have been studied. In fact, an ANFIS (Artificial Neuro Fuzzy Inference System) method has also been used, in addition to an alternative Neuro Fuzzy approach. Several trials have been carried out, using both techniques, obtaining satisfactory results after using these techniques.