• Proceedings of the KIPE Conference
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• 2009.11a
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• pp.135-138
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• 2009

In this paper, a novel maximum power point tracking (MPPT) for a PMSG-based variable speed wind power system is proposed using the fuzzy logic algorithm. The control algorithm is developed based on the normal hill climb searching (HCS) method, commonly used in wind energy conversion systems (WECS). The inputs of fuzzy-based controller are the derivations of DC output power and the step size of DC/DC converter duty cycles. The main advantages of the proposed MPPT method are no need to measure the wind velocity and the generator rotational speed. As such, the control algorithm is independent of turbine characteristics, achieving the fast dynamic responses with non-linear fuzzy systems. The effectiveness of the proposed MPPT strategy has been verified through the simulated results.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.11-19
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• 1996

Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.

• Journal of Biosystems Engineering
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• v.28 no.2
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• pp.117-126
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• 2003

This study was conducted to develop precision control system fur cold storage facility that could offer safe storage environment for green grocery. For that reason of neuro-fuzzy control system with learning ability algorithm and single chip neuro-fuzzy micro controller was developed for cold storage facility. Dynamic characteristics and hunting of neuro-fuzzy control system were far superior to on-off and fuzzy control system. Dynamic characteristics of temperature were faster than on-off control system by 1,555 seconds(123% faster) and fuzzy control system by 460 seconds(36.4% faster). When system was arrived at steady state. hunting was ${\pm}$0.5$^{\circ}C$ in on-off control system, ${\pm}$0.4$^{\circ}C$ in fuzzy control system, and ${\pm}$0.3$^{\circ}C$ in neuro-fuzzy control system. Hunting of humidity and wind velocity was also controlled precisely by 70 to 72.5% and 1m/s For storage experiment with onion, characteristics of neuro-fuzzy control system were tested. Dynamic characteristics of neuro-fuzzy control system made cold storage facility conducted precooling ability and minimized hunting.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.584-588
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• 1991

This paper presents a fuzzy adaptive controller which can improve the control policy automatically. Adaptation is achieved by the addition of on-line identification of the fuzzy inverse model using input-output data pairs of the process. Starting with an initial crude control rule, the adaptive controller matches the model to the process to self-tune the controller. The control algorithm needs much less memory of computer than other SOC algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.479-483
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• 1992

This paper defines the relationship between PI type fuzzy control system and conventional PI control system, and discusses the relationship of parameters and control action in fuzzy controller. The tuning algorithm that updates ouput variable scaling factor of fuzzy controller is proposed .The proposed sheme is applied to the simulations of 2 selected dynamical plants. The simulation results show that the controller is effective in controlling dynamical plants.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.18-21
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• 2014

We propose a nonlinear observer design method for traffic control systems based on T-S fuzzy approach. We parameterize the observer gains in terms of the solution matrices of LMIs. We also give a simple algorithm to compute the observer gain matrices. Finally we give simulation results to show the effectiveness of the proposed fuzzy observer design method.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.403-405
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• 1997

In this paper, a navigation system based on fuzzy logic controllers is developed for a mobile robot in an unknown environment. The structure of this fuzzy navigation system features sensor system, fuzzy controllers for motion planning and the motion control system for real-time execution.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.268-271
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• 2001

This paper describes the power factor control of doubly fed induction machine using fuzzy logic algorithm in wind power generation system. Under fuzzy logic control, which enables superior dynamic performance, the power factor is independently controllable by decoupled d, q rotor experimental results are presented.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.2
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• pp.103-112
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• 2011

This paper proposes a new fuzzy speed controller for the precise speed control of a permanent magnet synchronous motor(PMSM). The proposed control system needs the information of the angular acceleration instead of the load torque, so the third-order fuzzy acceleration observer estimates it. Moreover, the LMI conditions are derived for the existence of the fuzzy acceleration observer and fuzzy speed controller, and the gain matrices of the observer and controller are obtained. It is analytically proven that the proposed observer-based fuzzy speed regulator is exponentially stable. To evaluate the performance of the proposed control algorithm, experimental results as well as simulation results are provided under the conditions of motor parameter and load torque variations. Finally, it is clearly confirmed that the proposed control method can accurately control the speed of a PMSM.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.701-706
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• 1998

A design method of rule -based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of " IF..., THEN,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.