• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.4
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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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.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.3
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.3
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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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• 2000.10a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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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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• v.15 no.4
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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.