• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.973-979
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• 2001

Sliding mode control guarantees robustness in the presence of modeling uncertainties and external disturbances. However, this can be obtained at the cost of high control activity that may lead to chattering As one way to alleviate this problem a boundary layer around sliding surface is typically used. In this case the selection of controller gain, control ban width and boundary layer thickness is a crucial problem for the trade-off between tracking error and chattering. The parameter tuning is usually done by trail-and-error in practice causing significant effort and time. An auto tuning method based on fuzzy rules is proposed in the paper in this method tracking error and chattering are monitored by performance indices and the controller tunes the design parameters intelligently in order to compromise both indices. To demonstrate the efficiency of the propose method a mass-spring translation system and a roboic control system are simulated and tested It is shown that the proposed algorithm is effective to facilitae the parameter tuning for sliding mode controllers.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.6
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• pp.30-39
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• 2014

Through a lot of papers, it has been concluded that fuzzy logic controller is superior to PI controller in motor speed control. Although fuzzy logic controller is superior to PI controller in motor speed control, the gain tuning of fuzzy logic controller is more complicated than that of PI controller. In this paper, using mathematical analysis of the PI and fuzzy controller, the design method of the fuzzy controller that has the same characteristics with the PI controller is proposed. After that, we can design the fuzzy controller that has superior performance than PI controller by changing the envelope of input of fuzzy controller to nonlinear, because the fuzzy controller has more degree of freedom to select the control gain than PI controller. The advantage of fuzzy logic controller is shown through mathematical analysis, and the simulation result using Matlab simulink has been proposed to show the effectiveness of these analysis.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.8-13
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• 2001

In the turning process, the feed is usually selected by a machining operator considering workpiece, cutting tool and depth of cut. Even if this selection can avoid power saturation or tool breakage, it is usually conservative compared to the capacity of the machine tools and can reduce the productivity significantly. This paper proposes a selection method of the feed and the reference cutting force based on MRR(material removal rate), maximum spindle power and specific energy. In order to estimate and control cutting force accurately in transient and steady state, this study utilizes a synthesized cutting force estimation method and a Fuzzy controller. The experimental results present that these systems can be useful for the FMS(flexible manufacturing system) and unmanned automation system.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.531-533
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• 1999

The main aim of this paper is to investigate the possibility of applying fuzzy control algorithms to a microprocessor-based servomotor controller which requires faster and more accurate response compared with many other industrial processes. In addition, this study deals with the control of the lateral motion of a mobile vehicle. A adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve control of the lateral motion of the vehicle.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.328-330
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• 2005

Most of the water level controllers of the actual plant are PID controllers. But they have limitations in appling for tracking the set point and getting rid of disturbances, so there are some defects to apply in the actual ground even though many research works represented the resolution to solve it. In this paper, we design a fuzzy logic system (FLS) for controlling the steam generator water level in nuclear power plants. Some computer simulations reveal similar performance with the conventional PID controller.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.4
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• pp.393-399
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• 2003

In this paper, a self-organizing fuzzy logic controller with hybrid structure is proposed. The structure of the proposed method is composed of a basic fuzzy logic controller and the FARMA SOC(Fuzzy Autoregressive Moving Average Self-organizing Controller). The self-organizing cntroller with hybrid structure has advantage over the FARMA controller as follows. The proposed controller improves poor performance due to the lack of I/O data to calculate predictive output. I executed some computer simulations on the regulation problem of an inverted pendulum system and compared the results of the proposed method with those of the FARMA SOC method.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.539-541
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• 1997

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. If the rules adequately control the system, the design work is done well. If the rules are inadequate, the designer must modify the rules. Through this procedure, the system can be controlled. In this paper, we design fuzzy controller composed of two parts, one is balancing controller, the other is angle controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.63-66
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• 1997

The rule bases self organizing controller(SOC) has one of its main advantages in the fact that there is no need to have a mathematical description of the system to be controlled. In this controller, the rules are linguistics statements expressed mathematically through the concepts of fuzzy sets and correspond to the actions a human operator would take when controlling a given process. With this controller, we have performed to sight stabilization system, and we realize that it needs a scale factor tuning. The self tuning controller(STC) uses an instantaneous system fuzzy performance which can give an inspection to the scale factor. Therefore, the STC can compensate the scale factor when it is not adequately tuned. With this trial, we shows that STC can give a good transient characteristics in the nonlinearity which imposed basically in the conventional servo system.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.3
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• pp.23-31
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• 1994

In this paper, the yo-yo control system is introduced as a new benchmark system for evaluation of intellignet controllers. In order to control an yo-yo, and asymmetric nonlinear controller is needed due to the unique nonlinear asymmetric dynamic characteristics of the system. As such, it is difficult to control an yo-yo either by a linear controller or by a bang-bang controller. In the paper, we have inplemented a yyo control system with a general=purpose fuzzy controller. In the fuzzy control, 14 if-then rules are used, being extracted from human experties and, for real-time control, a fuzzy inference hardware(called FLEXi) is used.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.1
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• pp.24-34
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• 1994

A RMFSOC(Reference Model Following Self-Organizing Fuzzy Logic Controller) is propose in this paper. In the RMFSOC, the refernce model is introduced, where the desired control performance can be specified by an operator of the controlled process. The self-organizing level of the RMFSOC organizes the control rules of FLC which make the process output follow the reference model output. In addition, for the use of preventing improper modifications of control rules, a complementary decission rule is induced from the possible relations between the process output and reference model output. Through a simulation study, it is shown that the robustness of the control system using the proposed RMFSOC to the set-point changes and distur bances can be greatly improved being conpared with that of the control system using the Procyk and Mamdani's SOC.