• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.140-142
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• 2001

Three single loop fuzzy logic controllers are designed independently for the control of boiler system of fossil-power plant. The control rules and the membership functions of proposed fuzzy logic control system are generated automatically without using plant model. The simulation shows successful results for wide range operation of boiler system of fossil-power plant.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.524-527
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• 2003

This paper proposes speed control system using a Fuzzy Logic Controller(FLC) in order to realize the speed control of Permanent Magnet AC Motor with no sensor. FLC based MRAS(Model Reference Adaptive System) estimates the speed of Permanent Magnet AC Motor. Using the estimated speed, speed control is performed. The experiment is executed to verify the propriety and the effectiveness of the proposed system.

• International Journal of Automotive Technology
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• v.4 no.4
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• pp.181-191
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• 2003

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, this paper proposes a new control strategy for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. In the control scheme, the PID controller is used to minimize vehicle body vertical acceleration, the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to improve the adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, the gain values and the membership functions of fuzzy logic controller on-line. Taking a four degree-of-freedom nonlinear vehicle model as example, the proposed control scheme is applied and the simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and in attenuating the excessive dynamic tire load to enhance road holding performance. The stability and adaptability are also showed even when the system is subject to severe road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and the active vehicle suspension systems by using, e.g., linear fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability can improve vehicle ride comfort performance significantly and offer better system robustness.

• Journal of Biosystems Engineering
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• v.25 no.2
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• pp.107-114
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• 2000

Low temperature storage method is to increase the value of agricultural products by reducing quality loss and regulate consignment time by controlling respiration rates of agricultural products. Respiration rate of agricultural products depends on several factors such as temperature, moisture, gas composition and a microbe inside the storage room. Temperature is the most important factor among these, which affects respiration rate and causes low or high temperature damage. Fuzzy logic was used to control the temperature of a storage room ,which uses information of uncertain facts and mathematical model for room temperature control . Room temperature was controlled better by using fuzzy logic control method rather than on-off control method. Refrigerant flow rates and temperature deviations were measured for on-off system using TEV(temperature expansion valve) and for fuzzy system using EEV(Electrical Expansion Valve) . Temperature of the Storage room was lowered faster by using fuzzy system than on -off system. Temperature deviation was -0.6~+0.9$^{\circ}C$ for on-off system and $\pm$0.2$^{\circ}C$ for fuzzy system developed. Temperature deviation and variation of temperature deviation were used as inout parameters for fuzzy system. The most suitable input and output value were found by experiment. Cooling rate of the storage room decreased while temperature deviation increased for the sampling time of 20 sec.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.704-707
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• 1997

In this paper, we present a fuzzy logic based tuner for continuous on-line tuning of PID controllers. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a singler parameter .alpha., then to use an on line fuzzy logic to self-tune the parameter. The adaptive scaling makes the controller robust against large variations in parametric and dynamics uncertainties in the plant model. New self-tuning controller has the ability to decide when to use PI or PID control by extracting process dynamics from relay experiments. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with nonminimum phase processes.

• Journal of Drive and Control
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• v.17 no.1
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• pp.44-50
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• 2020

For the requirement of accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Because of the complexity of humanoid robot dynamics, the TDC (time-delay control) is practical because it does not require a dynamic model. However, there occurs a considerable error due to discontinuous non-linearities. To solve this problem, the TDC-FLC (fuzzy logic compensator) is applied to humanoid robots. The applied controller contains three factors: a TDE (time-delay estimation) factor, a desired error dynamic factor, and FLC to suppress the TDE error. The TDC-FLC is easy to execute because it does not require complicated humanoid dynamic calculations and the heuristic fuzzy control rules are intuitive. TDC-FLC is implemented on the whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the TDC-FLC for humanoid robots.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1112_1113
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• 2009

This paper proposes the method to predict the wind turbine power generation using logic-based fuzzy neural networks. To predict the wind turbine power generation neural networks, logic-based fuzzy neural networks, and fuzzy neural models have been considered. But the model considered in this paper can predict the wind turbine power generation with a less complex structure. The simulation results show the effectiveness of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.3 s.18
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• pp.140-148
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• 2004

Fault detection and isolation(FDI) and reconfigurable flight control system provide better survivability even though actuator faults occur. In this study, a new fault detection and isolation algorithm is proposed using fuzzy logic. When the FDI system detects the actuator fault, the fuzzy logic investigates the state variables to find which actuator has fault. Proposed fuzzy detection algorithm detect not only a single fault but also multiple faults. After detecting the fault, the reconfigurable flight control system begins operating for compensating the effects of the fault. A numerical simulation using six degree-of-freedom nonlinear aircraft model is performed to verity the performance of the proposed fault detection and isolation scheme.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2007.06a
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• pp.244-249
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• 2007

In this paper, in order to get the characteristics of the lithium secondary cell, such as cycle life, charge and discharge characteristic, temperature characteristic, self-discharge characteristic and the capacity recovery rate etc, we build a mathematical model of battery. In this one-dimensional model, Seven governing equations are made to solve seven variables $c,\;c_s,\;{\Phi}_1,\;{\Phi}_2,\;i_2,\;j\;and\;T$. The mathematical model parameters used in this model have been adjusted according to the experimental data measured in our lab. The connecting research of this study is to get an accurate estimate of the capacity of battery through comparison of results from simulation and fuzzy logic system. So the result data from this study is reorganized to fit the fuzzy logic algorithm.