• 한국정밀공학회지
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• 제15권10호
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• pp.180-192
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• 1998

This paper introduces a new self-organizing fuzzy logic controller (SOFLC) for precision contour machining in the presence of large disturbances which adjusts both input and output membership functions simultaneously. The parameters of the proposed controller are self-tuned in real-time according to a continuous measurement of the performance of the controller itself and estimated disturbance values. The proposed controller as well as a conventional fuzzy logic controller and a PID controller were simulated and implemented on a 3-axis milling machine in contour milling. Both the simulations and experiments show that the self-organizing fuzzy logic controller has superior performance in terms of contour tracking accuracy compared with the other two controllers.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.1058-1062
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• 2003

This paper proposes a self-organizing fuzzy controller (SOFC) design technique applied to the vibration control of a dynamic system under irregular disturbance. In this controller, the fuzzy rules generate control signal continuously using the array of input and output pairs without using any special controller model. The generated rules are saved in the fuzzy rule matrix in real-time by self-organizing methods. This fuzzy logic control is demonstrated by simulation and shows the efficiency of the real-time self-organizing fuzzy controller in this system.

• 한국산업융합학회 논문집
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• 제6권4호
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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.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권5호
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• pp.353-360
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and environment as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to partition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum on the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

• Journal of Microbiology and Biotechnology
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• 제11권5호
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• pp.739-748
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• 2001

A self-organizing fuzzy logic controller using a genetic algorithm is described, which controlled the glucose concentration for the enhancement of flavonoid production in a fed-batch cultivation of Scutellaria baicalensis G. plant cells. The substrate feeding strategy in a fed-batch culture was to increase the flavonoid production by using the proposed kinetic model. For the two-stage culture, the substrate feeding strategy consisted of a first period with 28 g/I of glucose to promote cell growth, followed by a second period with 5 g/I of glucose to promote flavonoid production. A simple fuzzy logic controller and the self-organizing fuzzy logic controller using a genetic algorithm was constructed to control the glucose concentration in a fed-batch culture. The designed fuzzy logic controllers were applied to maintain the glucose concentration at given set-points of the two-stage culture in fed-batch cultivation. The experimental results showed that the self-organizing fuzzy logic controller improved the controller\`s performance, compared with that of the simple fuzzy logic controller. The specific production yield and productivity of flavonoids in the two-stage culture were higher than those in the batch culture.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.191-196
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• 1998

A new Fuzzy Logic Controller(FLC) called a Gradient-Descent Based Self-Organizing Controller is presented. The Self-Organizing Controller(SOC) has two inputs such as error and change of error, and updates control rules with monitoring a performance measure. There are many works in the SOC which concentrate on the self-organizing ability in control rule base, but have a few research on the performance measure which is akin to sliding mode control. With this procedure, we can get a robust performance measure on the SOC. To verify the perfomance of proposed controller, we have performed for the cart-pole system which is one of the well-known benchmark problem in the control literature.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.681-686
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• 1991

In this paper, a novel self-organizing fuzzy plus PID control algorithm is proposed and analyzed by extensive computer simulations and experiments with an inverted pendulum. Specifically, the proposed self-organizing fuzzy controller consists of a typical fuzzy reasoning part and self organizing part in which both on-line and off-line algorithms are employed to modify the 'then' part of the fuzzy rules and to decide how much fuzzy rules are to be modified after evaluating the control performance, respecfively. And the fuzzy controller is replaced by a PID controller in a prespecified region near by the set point for good settling actions.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1223-1232
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• 2004

We present a new technique to the design and real-time implementation of fuzzy control system basedon digital signal processors in order to improve the precision and robustness for system of industrial robot in this paper. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C80 is used in implementing real time fuzzy control to provide an enhanced motion control for robot manipulators. In this paper, a Self-Organizing Fuzzy Controller for the industrial robot manipulator with a actuator located at the base is studied. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a Fuzzy Logic Controller, one of the most difficult problems is the determination of linguistic control rules from the human operators. To overcome this difficult Self-Organizing Fuzzy Controller is proposed for a hierarchical control structure consisting of basic and high levels that modify control rules. The proposed Self-Organizing Fuzzy Controller scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the SOFC is illustrated by simulation and experimental results for a Dual-Arm robot with eight joints.

• 산업기술연구
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• 제24권A호
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• pp.99-107
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and the environments as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to parition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum of the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.