• Journal of Electrical Engineering and Technology
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• 제4권1호
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• pp.1-12
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• 2009

In recent years, integration of new distributed generation (DG) technology in distribution networks has become one of the major management concerns for professional engineers. This paper presents a dynamic methodology of optimal allocation and sizing of DG units for a given practical distribution network, so that the cost of active power can be minimized. The approach proposed is based on a combined Genetic/Fuzzy Rules. The genetic algorithm generates and optimizes combinations of distributed power generation for integration into the network in order to minimize power losses, and in second step simple fuzzy rules designs based upon practical expertise rules to control the reactive power of a multi dynamic shunt FACTS Compensator (SVC, STATCOM) in order to improve the system loadability. This proposed approach is implemented with the Matlab program and is applied to small case studies, IEEE 25-Bus and IEEE 30-Bus. The results obtained confirm the effectiveness in sizing and integration of an assigned number of DG units.

• 대한전기학회논문지:전력기술부문A
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• 제49권2호
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• pp.62-69
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• 2000

In this paper, we design a GA-fuzzy precompensated PID controller for the load frequency control of two-area interconnected power system. Here, a fuzzy precompensated PID controller is designed as a fuzzy logic-based precompensation approach for PID controller. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing PID controller. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and control rules. Simulation results show that the proposed control technique is superior to a conventional PID control and a fuzzy precompensated PID control in dynamic responses about the load disturbances of power system and is convinced robustness reliableness in view of structure.

• 전자공학회논문지B
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• 제30B권12호
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• pp.87-97
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• 1993

To improve the limitation of fuzzy PI controller when is applied to systems of order higher than one, a fuzzy PI controller that fuzzily resets or amplifies the accumulated control input according to fuzzy rules defined on (error, change of error) space is proposed. The proposed controller structure was motivated by the characteristics of fuzzy PI controller that it generally gives unevitable large overshoot in trial of reducing rise time of response especially when a system of order higher than one is considered. Based on the observation that the undesirable characteristics of the fuzzy PI controller is caused by integrating control input excessively, even though the integrator is introduced to overcome steady state error, we propose a controller that clear out or doubles integrated control input in a fuzzy manner according to the situation to reduce rise time as well as overshoot. To show the usefulness of the proposed controller, it is applied to the systems that are difficult to stabilize or difficult to get satisfactory response by conventional fuzzy PI controllers.

• 한국지능시스템학회논문지
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• 제3권2호
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• pp.58-67
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• 1993

Instead of Cartesian product in combining multiple input variables for fuzzy logic controllers, a fuzzy controller using fuzzy relations in inference procedure is proposed. Moreover, a technique is proposed by which conventional fuzzy control rules are transformed into the forms including fuzzy relations. It will be shown through several examples that the proposed technique gives smoother interpolation than conventional ones.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.669-675
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• 1991

Based on process analysis as well as extensive operation experience, two fuzzy expert control algorithms, for startup and control, are proposed for a supercritical fluid extraction process which has high interacting multivariable structure. In the proposed algorithms, a new simple defuzzification method which only requires four fundamental arithmetic rules is also presented. Through numerical simulations, control performance using the proposed control algorithm is compared with that of a different fuzzy algorithm by an other researcher and that of conventional PID-type controllers which are tuned by well-known optimal criteria. Also, the proposed control algorithm has been tested to the bench scale supercritical fluid extraction process. As a consequence, the proposed fuzzy expert controller has shown fast and robust control performance while the other controllers show sluggish and/or highly oscillatory responses.

• International Journal of Control, Automation, and Systems
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• 제2권1호
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• pp.76-82
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• 2004

A robust fuzzy controller is designed to stabilize a class of solvable nonlinear descriptor systems with time-varying delay. First, a new modeling and control method for nonlinear descriptor systems is presented with a fuzzy descriptor model. A sufficient condition for the existence of the fuzzy controller is given in terms of a series of LMIs. Then, a less conservative fuzzy controller design approach is obtained based on the fuzzy rules and weights. This method includes the interactions of the different subsystems into one matrix. The effectiveness of the presented approach and the design procedure of the fuzzy controller are illustrated by way of an example.

• 제어로봇시스템학회논문지
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• 제7권6호
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• pp.509-517
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• 2001

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require and accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is evaluated through a series of experiments for the various types of inputs while applying disturbances to the hydraulic system. The performance of this controller was compared with those of PID and PD controllers. From these results, We observe be said that the position tracking performance of neuro-fuzzy is better those of PID and PD controllers.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.43-48
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• 2010

This article presents the experimental studies of controlling angle and position of the inverted pendulum system using neural network to compensate for errors caused due to fuzzy controller. Although fuzzy control method can deal with nonlinearities of the system, fixed fuzzy rules may not work and result in tracking errors in some cases. First, a nominal Takagi-Sugeno (TS) type fuzzy controller with fixed weights is used for controlling the inverted pendulum system. Then the neural network is added at the reference input to form the reference compensation technique (RCT)control structure. Neural network modifies the input trajectories to improve system performances by updating internal weights in on-line fashion. The back-propagation learning algorithm for neural network is derived and used to update weights. Control hardware of a DSP 6713 board to have real time control is implemented. Experimental results of controlling inverted pendulum system are conducted and performances are compared.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.822-825
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• 1993

A fuzzy logic controller derived from the variable structure control (VSC) theory is designed. Unlike the conventional design of the fuzzy controller, we do not fuzzify the error and the rate of error, but fuzzify the sliding surface. After the fuzzy sliding surface is introduced, the fuzzy rules are defined based on the sliding control theory. It will be shown this sliding mode fuzzy controller is a kind of VSC that introduces the boundary layer in the switching surface and that the control input is continuously approximated in the layer. As a result we can guarantee the stability and the robustness by the help of VSC, which were difficult to insure in the past fuzzy controllers. Simulation results for the inverted pendulum will show the validity.