• 연구논문집
• /
• s.23
• /
• pp.73-80
• /
• 1993

This study presents an intelligent PID control method based on the fuzzy logic and this method is applied to the active magnetic bearing system. By using an appropriate fuzzy matrix, some changes of values of the three coefficients of the controller are determined during system operation and these lead to the improvement of the transient and steady state behavior of the closed loop system. The presented method is actually a combination of the principles of PID control and fuzzy logic. Since the fuzzy logic using linguistic variables in place of numeric variables has many points of likeness to the human logic, the improvement in performance is notable especially in case of large nonlinearity and uncertainty such as the controller start and the excessive mass unbalance. A set of simulation and experimental results illustrate and considerable improvement in the control performance including small overshoot and small transient currents in magnet coils, while maintaining the overal static and dynamic characteristics near the equilibrium position.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.897-899
• /
• 1999

This paper presents a scaling factor tuning method to improve the performance of fuzzy logic controller. Tuning rules and reasoning are utilized off-line to determine the scaling factors based on absolute value of the error and its difference. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a dc-servo motor control system. The performance of this control system is demonstrated higher than a conventional fuzzy logic controller(FLC).

• Proceedings of the KIEE Conference
• /
• 2015.07a
• /
• pp.845-847
• /
• 2015

A suspending force control based on fuzzy logic control is proposed to apply on a novel hybrid bearingless switched reluctance motor(BLSRM) which has separated torque and suspending force pole. Due to the unique structure, the suspending force control system can be easily decoupled from torque control system. In this paper, two fuzzy controller targeted at x-axis direction and y-axis direction are adopted to maintain the shaft at center position, which is very necessary for stable operation of BLSRM. By replacing the traditional PI block with modified fuzzy logic controller, the suspending system can behave a good performance, and the proposed scheme can be verified by simulation results.

• Journal of Power Electronics
• /
• v.12 no.2
• /
• pp.305-316
• /
• 2012

The paper presents an accurate loss model based controller of an induction motor to calculate the optimal air gap flux. The model includes copper losses, iron losses, harmonic losses, friction and windage losses, and stray losses. These losses are represented as a function of the air gap flux. By using the calculated optimal air gap flux compared with rated flux for speed sensorless indirect vector controlled induction motor, an improvement in motor efficiency is achieved. The motor speed performance is improved using a fuzzy logic speed controller instead of a PI controller. The fuzzy logic speed controller was simulated using the fuzzy control interface block of MATLAB/SIMULINK program. The control algorithm is experimentally tested within a PC under RTAI-Linux. The simulation and experimental results show the improvement in motor efficiency and speed performance.

• Proceedings of the KIEE Conference
• /
• 1997.07c
• /
• pp.838-840
• /
• 1997

This presents a design method for fuzzy logic controllers of TCSC using genetic algorithm. Fuzzy logic controllers are applied to damp the dynamic disturbances sum as sudden changes of AC system loads. The dynamic performances of fuzzy logic controllers are compared with those of PI controllers. The simulation results show that dynamic performances of fuzzy controllers have better response than those of PI controllers when the AC system load changes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1996.10a
• /
• pp.243-248
• /
• 1996

In this paper, a general neural-network-based connectionist model, called Fuzzy Neural Network(FNN), is proposed for the realization of a fuzzy logic control system. The proposed FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such FNN can be constructed from training examples by learning rule, and the connectionist structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Computer simulation examples will be presented to illustrate the performance and applicability of the proposed FNN, and their associated learning algorithms.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.3 no.4
• /
• pp.48-59
• /
• 1993

Intellignet control of the dyeing machine is a central part to improve the productivity of autonomous dyeing systems. Recently, many number of control methods are introuduced. One of them is fuzzy logic algorithm. Fuzzy logic based controller has many desirable advantages, which are simple to implement on the real time and need not the information of dynamic characteristics of the systems. In this paper we propose a new dyeing machine control simulator using fuzzy logic algorithm as an approach to develop the intellingent auto-dyeing control system. This developing approach of the fuzzy control simulator is based on linguistic control stratege of experts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.05a
• /
• pp.322-325
• /
• 2002

We proposed a method to improve KCN's problems. Proposed method adjusts neighborhood and teaming rate by fuzzy logic system. The input of fuzzy logic system used a distance and a change rate of distance. The output was used by site of neighborhood and learning rate. The rule base of fuzzy logic system was taken by using KCN simulation results. We used Anderson's Iris data to illustrate this method, and simulation results showed effect of performance.

• Journal of information and communication convergence engineering
• /
• v.4 no.1
• /
• pp.36-40
• /
• 2006

A new method of digital image analysis technique for medical images of cancer cell is presented. This paper deals with the cancer cell discrimination. The object images were the Thyroid Gland cell images that were diagnosed as normal and abnormal. This paper proposes a new discrimination method based on fuzzy logic algorithm. The focus of this paper is an automatic discrimination of cells into normal and abnormal of medical images by dominant feature parameters method with fuzzy algorithm. As a consequence of using fuzzy logic algorithm, the nucleus were successfully diagnosed as normal and abnormal. As for the experimental result, average recognition rate of 64.66% was obtained by applying single parameter of 16 feature parameters at a time. The discrimination rate of 93.08% was obtained by proposed method.

• 한국정보통신설비학회:학술대회논문집
• /
• 2008.08a
• /
• pp.177-180
• /
• 2008

There are many application areas of sensor networks, such as surveillance, hospital monitoring, and home network. These are dependent on the secure operation of networks, and will have serious outcome if the networks is injured. An adversary can inject false data into the network through the compromising node. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false data during forwarding process. In this scheme, it is important that the choice of the threshold value since it trades off security and overhead. This paper presents an adaptive threshold value determining method in the SEF using fuzzy logic. The fuzzy logic determines a security distance value by considering the situation of the network. The Sensor network is divided into several areas by the security distance value, it can each area to uses the different threshold value. The fuzzy based threshold value can reduce the energy consumption in transmitting.