• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.268-271
• /
• 1998

this paper presents the potential application of fuzzy logic to the automatic incident detection system. While the conventional incident detection algorithms are based on a binary decision process, the algorithm using fuzzy logic can incorporate ambiguity which occurs in determining incidents. Since collecting good amount of data to construct data base for incidents is pretty expensive, a traffic simulator called FRESIM is used to simulate traffic condition in a freeway. Incident data are obtained by changing input parameters of the simulator and the fuzzy algorithm generates fuzzy rule for determining normal and incident traffic conditions. In this paper, various steps are described to test the algorithm and its results are summarized.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.47-51
• /
• 2001

According as the controlled plants become more complex and large-scaled, the development of more intelligent control schemes is required in the control field. A fuzzy logic control (FLC) is one of proper schemes for this tendency. Recently, fuzzy control has been applied successfully to many industrial applications due to a number of advantages. But it still has some disadvantages. The conventional FLC has many tuning parameters: membership functions, scaling factors, and so forth. In order to improve this problem, a single-input fuzzy logic control (SFIC) which greatly simplifies the design process of the conventional FLC was proposed. Many research has also been proposed to develop the stability analysis of the FLC. In this paper we analyze the absolute stability of the SFLC. We first expand a nonlinear controlled plant into a Taylor series about a nominal operating point. And a fuzzy control system is transformed into a Lure system with nonlinearities. We also prove that the closed-loop system with the SFLC satisfies the sector condition globally.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.12 no.3
• /
• pp.238-244
• /
• 2012

A modeling of vision based robot formation control system using fuzzy logic controller and extended Kalman filter is presented in this paper. The main problems affecting formation controls using fuzzy logic controller and vision based robots are: a robot's position in a formation need to be maintained, how to develop the membership function in order to obtain the optimal fuzzy system control that has the ability to do the formation control and the noise coming from camera process changes the position of references view. In order to handle these problems, we propose a fuzzy logic controller system equipped with a dynamic output membership function that controls the speed of the robot wheels to handle the maintenance position in formation. The output membership function changes over time based on changes in input at time t-1 to t. The noises appearing in image processing change the virtual target point positions are handled by Extended Kalman filter. The virtual target positions are established in order to define the formations. The virtual target point positions can be changed at any time in accordance with the desired formation. These algorithms have been validated through simulation. The simulations confirm that the follower robots reach their target point in a short time and are able to maintain their position in the formation although the noises change the target point positions.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.2
• /
• pp.97-106
• /
• 1999

This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.6
• /
• pp.1978-1987
• /
• 2014

Power requirement to the induction heating system varies during the heating process. A closed loop control is required to have a smooth control over the power. In this work, a constant frequency pulse density modulation based power tracking control scheme for domestic induction heating system is developed using the Fuzzy Logic Controller. In the conventional power modulation schemes, the switching losses increase with the change in the load. The proposed pulse density modulation scheme maintains minimum switching losses for the entire load range. This scheme is implemented for the class-D series resonant inverter system. Fuzzy logic controller based power tracking control scheme is developed for domestic induction heating power supply for various power settings. The open loop and closed loop simulation studies are done using the MATLAB/Simulink simulation tool. The control logic is implemented in hardware using the PIC16F877A microcontroller. Fuzzy controller tracks the set power by changing the pulse density of the gate pulses applied to the inverter. The results obtained are used to know the effectiveness of the fuzzy logic controller to achieve the set power.

• 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.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2000.11b
• /
• pp.354-362
• /
• 2000

In Korea, researches on year-round leaf vegetables production system are in progress, most of them focused on environmental control. Therefore, automation technologies for harvesting, transporting, and grading are in great demand. A robot system for harvesting lettuces, composed of a 3-DOF (degree of freedom) manipulator, an end-effector, a lettuce feeding conveyor, an air blower, a machine vision system, six photoelectric sensors, and a fuzzy logic controller, was developed. A fuzzy logic control was applied to determine appropriate grip force on lettuce. Leaf area index and height were used as input variables and voltage as an output variable for the fuzzy logic controller. Success rate of the lettuce harvesting was 94.12%, and average harvesting time was approximately 5 seconds per lettuce.

• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.491-494
• /
• 1997

In this paper, we suppose a fuzzy logic controller for cruise control of vehicle. Generally, fuzzy logic controller is known as a controller which can be coped with a non-linear and a complex system. The proposed fuzzy logic controller consists of three input variables; that is, a desired speed, a current vehicle speed, and a current acceleration, and one output variable, throttle angle. The supposed fuzzy logic controller is for engine speed control system is implemented on 80586 microprocessor with DT-2801.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.43 no.3
• /
• pp.468-477
• /
• 1994

Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unkonwn deadzones. In particular, we show that a conventional fuzzy logic controller applied to a system with a deadzone suffers from poor transient performance and a large steady-syate error. In this paper, we propose a novel two-layered fuzzy logic controller for controlling systems with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator followed by a conventional fuzzy logic controller. Our proposed controller exhibits superior transient and steady-state performance compared to conventional fuzzy controllers. In addition, the controller is robust to variations in deadzone nonlinearities. We illustrate the effectiveness of our scheme using computer simulation examples.

• 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.