• Journal of the Korean Society for Precision Engineering
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• v.15 no.9
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• pp.56-67
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• 1998

This paper introduces a friction compensation fuzzy logic controller, which utilizes a rule-based approach. The paper explains the algorithm of the proposed controller and compares it with a conventional PID controller in simulations and experiments. For the experiments, the two control algorithms were implemented on a 3-axis milling machine in contour milling. These simulation and experimental analyses show that the proposed fuzzy logic controller has superior performance over conventional PID controllers In terms of part contour accuracy.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.352-362
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• 2014

In this paper, a fuzzy logic controller is designed for a DC motor which can be used for navigation control of mobile robots. These mobile robots can be used for agricultural, defense and assorted social applications. The robots used in these fields can reduce manpower, save human life and can be operated using remote control from a distant place. The developed fuzzy logic controller is used to control navigation speed and steering angle according to the desired reference position. Differential drive is used to control the steering angle and the speed of the robot. Two DC motors are connected with the rear wheels of the robot. They are controlled by a fuzzy logic controller to offer accurate steering angle and the driving speed of the robot. Its location is monitored using GPS (Global Positioning System) on a real time basis. IR sensors in the robot detect obstacles around the robot. The designed fuzzy logic controller has been implemented in a robot, which depicts that the robot could avoid obstacle as well as perform its operation efficiently with remote online control.

• KIEAE Journal
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• v.14 no.3
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• pp.97-103
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• 2014

This study aimed at developing integrated logic for controlling heating device and openings of the double skin facade buildings. Two major logics were developed-rule-based control logic and artificial neural network based control logic. The rule based logic represented the widely applied conventional method while the artificial neural network based logic meant the optimal method. Applying the optimal method, the predictive and adaptive controls were feasible for supplying the advanced thermal indoor environment. Comparative performance tests were conducted using the numerical computer simulation tools such as MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation). Analysis on the test results in the test module revealed that the artificial neural network-based control logics provided more comfortable and stable temperature conditions based on the optimal control of the heating device and opening conditions of the double skin facades. However, the amount of heat supply to the indoor space by the optimal method was increased for the better thermal conditioning. The number of on/off moments of the heating device, on the other hand, was significantly reduced. Therefore, the optimal logic is expected to beneficial to create more comfortable thermal environment and to potentially prevent system degradation.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.46-50
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• 1997

In fuzzy logic control, static fuzzy rules cannot cope with significant changes of parameters of plants or environment. To solve this prohlem, self-organizing fuzzy control. neural-network-hased fuzzy logic control and so on have heen introduced so far. However, dynamically changed fuzzy rules of these schemes may make a fuzzy logic controller Fall into dangerous situations because the changed fuzzy rules may he incomplete or inconsistent. This paper proposes a new adaptive filzzy logic control scheme using a predictivc neural network. Although some parameters of a controlled plant or environment are changed, proposed fuzzy logic controller changes its decision outputs adaptively and robustly using unchanged initial fuzzy rules and the predictive errors generated hy the predictive neural network by on-line learning. Experimental results with a D<' servo-motor position control problem show that propnsed cnntrol scheme is very useful in the viewpoint of adaptability.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.803-805
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• 1999

Robust fuzzy logic control, PID control, and sliding mode controllers are designed to control the speed of a third order linear time-invariant model of a motor. The step response performance of each controller, applied to the motor plant, is Presented. We conclude fuzzy logic control can be a useful tool for the control engineering.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.3
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• pp.254-261
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• 2005

Time-optimal performances are analyzed in the sense of inner loop. A varying-time sharing thrusting logic is suggested as a new sequential paired thrusting logic for fast maneuvers of satellites with coupled thruster configuration. Its time-optimal maneuvering performance is compared with two conventional thrusting logics: separate thrusting logic and constant-time sharing sequential paired thrusting logic. It is found that the newly suggested varying-time sharing thrusting logic can be easily implemented by adjusting the conventional constant-time logic with its thrust on-time, while it can reduce the maneuvering time enormously as much as the separate thrusting logic. The performance of the logic is simulated on the agile maneuvering spacecraft model KOMPSAT-II.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.6
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• pp.40-48
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• 2013

Recently, many types of electric vehicles including a heavy duty vehicle have been developed and released because of the better fuel economy and less gas products. In this study, research about an electric bus which utilizes the wheel motor drive system was conducted. The wheel motor is a motor connected to the wheel directly only with a simple gear so that the developer can utilize the space efficiently and the whole system efficiency will be better because of simple structure. However, because it is different from former types of vehicles which use the differential gear, the development of the integrated control logic is required in order to meet the vehicle stability and driving performance. The developed control logic is composed with direct yaw moment control, regenerative braking control and slip control logics. It is compared to the control logics which does not consist of direct yaw moment control and slip control when the vehicle is exposed in tough situations. For the unification of the control logic, a few maps were developed and applied to determine the output torque of each motor according to the driving status. As a result, it is shown that the developed control logic is more safe and well follow the target speed than the other control logic applied simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.103-111
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• 2004

In this survey paper, we attempt to introduce the subjects of fuzzy logic and neural network technology for flight control systems based on completed and ongoing research programs other developed countries. Also, it is prepared with intention of providing the reader with an overview of related topics and a basic concepts of fuzzy logic and neural network control. The focus is on relatively practical control schemes realistically applicable in the area of flight control system design that could find its usage in the near future in our country. It is hoped that this paper will serve as a useful reference and even concepts provide solutions far current problems and future designs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.907-910
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• 1993

This paper describes the use of fuzzy control and decision making to simulate the control of traffic flow at an intersection. To show the value of fuzzy logic as an alternative method for control of traffic environments. A traffic environment includes the lanes to and from an intersection, the intersection, vehicle traffic, and signal lights in the intersection. To test the fuzzy logic controller, a computer simulation was constructed to model a traffic environment. A typical cross intersection was chosen for the traffic environment, and the performance of the fuzzy logic controller was compared with the performance of two different types of conventional control. In the hardware verifications, fuzzy logic was used to control acceleration of a model train on a circular path. For the software experiment, the fuzzy logic controller proved better than conventional control methods, especially in the case of highly uneven traffic flow between different directions. On the hardware si e of the research, the fuzzy acceleration control system showed a marked improvement in smoothness of ride over crisp control.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.342-345
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• 1995

This paper describes the design process and the experimental results of a fuzzy logic controller to control the tip position of a fixible-link manipulator, directly driven by a AC motor, with a large payload. The joint angle fuzzy logic controller is designed without a costly nonlinear system analysis of the flexible manipulator and the AC motor drive system. The state variables for the fuzzy logic controller are joint angle, joint velocity, link deflection, and link deflection velocity. The simulation and experimental results show that the joint position control is not satisfactory when the controller is designed under the assumption of no link flexibility and that stable joint position control and link vibration suppression can be cahieved with the fuzzy logic controller suggested in this paper.