• Journal of Communications and Networks
• /
• v.7 no.3
• /
• pp.337-352
• /
• 2005

In this paper, a fuzzy logic implementation of the random early detection (RED) mechanism [1] is presented. The main objective of the proposed fuzzy controller is to reduce the loss probability of the RED mechanism without any change in channel utilization. Based on previous studies, it is clear that the performance of RED algorithm is extremely related to the traffic load as well as to its parameters setting. Using fuzzy logic capabilities, we try to dynamically tune the loss probability of the RED gateway. To achieve this goal, a two-input-single-output fuzzy controller is used. To achieve a low packet loss probability, the proposed fuzzy controller is responsible to control the $max_{p}$ parameter of the RED gateway. The inputs of the proposed fuzzy controller are 1) the difference between average queue size and a target point, and 2) the difference between the estimated value of incoming data rate and the target link capacity. To evaluate the performance of the proposed fuzzy mechanism, several trials with file transfer protocol (FTP) and burst traffic were performed. In this study, the ns-2 simulator [2] has been used to generate the experimental data. All simulation results indicate that the proposed fuzzy mechanism out performs remarkably both the traditional RED and Adaptive RED (ARED) mechanisms [3]-[5].

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.4
• /
• pp.281-287
• /
• 2002

This paper investigates on the feasibility of fuzzy logic control for networked control systems. In order to evaluate its feasibility, a networked control system for motor speed control is implemented on a Profibus-DP network. The NCS consists of several inde-pendent, but interacting processes running on two separate stations. By using this NCS, the network-induced delay is analyzed to find the cause and effect of the delay. Furthermore, in order to prove the feasibility, the fuzzy logic controller's performance is compared with those of conventional PID controllers. Based on the experimental results, the fuzzy logic controller can be a viable choice far NCS due to its robustness against parameter uncertainty.

• Journal of Biosystems Engineering
• /
• v.25 no.1
• /
• pp.63-70
• /
• 2000

In Korea, researches for year-round leaf vegetables production system are in progress and the most of them are focused on environment control. Automation technologies for harvesting , transporting and grading need to be developed. This study was conducted to develop harvesting process automation system profitable to a competitive price. 1. Manipulator and end-effector are to be designed and fabricated , and fuzzy logic controller for controlling these are to be composed. 2. The entire system constructed is to be evaluated through a performance test. A robot system for harvesting a lettuce was developed. It was composed of a manipulator with 20DOF (degrees of freedom) an end-effector, a lettuce feeding conveyor , an air blower , a machine vision device, 6 photoelectric sensors and a fuzzy logic controller. A fuzzy logic control was applied to determined appropriate grip force on lettuce. Leaf area index and height index were used as input parameters, and voltage was used as output parameter for the fuzzy logic controller . Success rate of the lettuce harvesting system was 93.06% , and average harvesting time was about 5 seconds per lettuce.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.1
• /
• pp.48-55
• /
• 2005

Mobility of an indoor wheeled robot is affected by adhesion force that is related to various floor conditions. When the adhesion force between driving wheels and floor decreases suddenly, the robot begins slip. In order to overcome this slip problem, optimal slip velocity must be decided for stable movement of wheeled robot. First of all, this paper shows that conventional PI control can not be applied to a wheeled robot of the light weight. Secondly, proposed fuzzy logic is applied to the Takagi-Sugeno model for the configuration of fuzzy sets. For the design of Takagi-Sugeno model and fuzzy rule, proposed algorithm uses FCM(Fuzzy c-mean clustering method) algorithm. In additionally, this algorithm adjusts the driving torque for restraining re-slip. The proposed fuzzy logic controller(FLC) is pretty useful with prevention of the slip phenomena for the controller performance in the re-adhesion control strategy, These procedures are implemented using a Pioneer 2-DXE wheeled robot parameter.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.51 no.10
• /
• pp.483-494
• /
• 2002

In this thesis an optimal design technique of fuzzy logic controller using the real variable elitist genetic algorithm(RVEGA) as a supplementary control to Static Var Compensator(SVC) in order to damp oscillation in an AC-DC Dower system was proposed. Fuzzy logic controller is designed self-tuning shape of fuzzy rule and fuzzy variable using genetic algorithm based on natural selection and natural genetics. To verify the robustness of the proposed method, considered dynamic response of system by applying a load fluctuation.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.12
• /
• pp.2285-2290
• /
• 2008

Even though efficiency of coal-fired power plant is proportional to operating temperature, increasement of operating temperature is limited by a technological level of each power plant component. It is an alternative plan to increase operating pressure up to ultra super critical point for efficiency enhancement. It is difficult to control process of power plant in ultra super critical point because that point has highly nonlinear characteristics. In this paper, new control logic, Unit Response Optimizer Controller(URO Controller) which is based on Fuzzy logic and Model Predictive Controller, is introduced for better performance. Then its performance is tested and analyzed with design guideline.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.459-464
• /
• 1998

In this paper, we propose a stable fuzzy logic controller architecture for inverted pendulum,. In the design procedure, we represent the fuzzy system as a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller by considering each local state feedback controller and a supervisory controller, Unlike usual parallel distributed controller, one can design a global stable fuzzy controller without finding a common Lyapunov function by the proposed method. A simulation is performed to control the inverted pendulum to show the effectiveness and feasibility of the proposed fuzzy controller.

• Proceedings of the KIEE Conference
• /
• 2006.07b
• /
• pp.1051-1052
• /
• 2006

This paper deals with mathmatical of an induction motor, considering non-linearity in the torque balance equation under closed loop operation with a reference speed. A controller based on Adaptive Nuro-Fuzzy Inference System (ANFIS) is developed to minimize overshoot and settling time following sudden changes in load torque. The overall system is modeled and simulated using the Matlab/simulink and Fuzzy Logic Toolbox. The advantages of fuzzy logic and neural network based fuzzy logic controller. Required training data the ANFIS controller is generated by simulation of the anti-windup PI controller is eliminated using the ANFIS controller. The transient deviation of the response from the set reference following variation in load torque is found to be negligibly samll along with a desirable reduction in settling time for the ANFIS controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.993-996
• /
• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

• Proceedings of the KIEE Conference
• /
• 2002.07b
• /
• pp.1172-1174
• /
• 2002

An adaptive fuzzy P + ID controller for variable speed operation of BLDC motor drives is presented in this paper. Generally, a conventional PID controller is most widely used in industry due to its simple control structure and ease of design. However, the PID controller suffers from the electrical machine parameter variations and disturbances. To improve the tracking performance for parameter and load variations, the controller proposed in this paper is constructed by using an adaptive fuzzy logic controller in place of the proportional term in a conventional PID controller. For implementing this controller, only one additional parameter has to be adjusted in comparison with the PID controller. An adaptive fuzzy controller applied to proportional term to achieve robustness against parameter variations has simple structure and computational simplicity. The controller based on optimal fuzzy logic controller has an self-tuning characteristics with clustering. Computer simulation results show the usefulness of the proposed controller.