• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.825-829
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• 2005

This paper presents an approach for designing a fuzzy logic-based adaptive SVC damping In controller for damping low frequency power oscillations. Power systems are often subject to low Frequency electro-mechanical oscillations resulting from electrical disturbances. Generally, power system stabilizers are designed to provide damping against this kind of oscillations. Another means to achieve damping is to design supplementary damping controllers that are equipped with SVC. Various approaches are available for designing such controllers, many of which are based on the concepts of damping torque and others which treat the damping controller design as a generic control problem and apply various control theories on it. In our proposed approach, linear optimal controllers are designed and then a fuzzy logic tuning mechanism is constructed to generate a single control signal. The controller uses the system operating condition and a fuzzy logic signal tuner to blend the control signals generated by two linear controllers, which are designed using an optimal control method. First, we design damping controllers for the two extreme conditions; the control action for intermediate conditions is determined by the fuzzy logic tuner. The more the operating condition belongs to one of the two fuzzy sets, the stronger the contribution of the control signal from that set in the output signal. Simulation studies done on a one-machine infinite-bus and a four-machine two-area test system, show that the proposed fuzzy adaptive damping SVC controller effectively enhances the damping of low frequency oscillations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.112-116
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• 1992

In this paper, a fuzzy logic controller(FLC) is designed for the pointing loop of the spin-stabilized platform. For the fuzzy inference, a fuzzy accelerator board using the Togai InfraLogic software and digital fuzzy processor(DFP110FC) is designed, and a validation of an algorithm for fuzzy logic control is also presented. The pointing loop of the spin-stabilized platform using FLC has better performance of step responses than a proportional controller in case of same loop hain through the software simulation and the experiment of implemented hardware.

• 제어로봇시스템학회논문지
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• 제6권1호
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• pp.104-111
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• 2000

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.532-535
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• 1997

This paper describes the design of a fuzzy-logic controller for a differential-drive mobile robots. This controller uses absolute position information to modify control parameters to compensate the orientation error. CC-Control method is compensated for the internal error by wheel encoders and the fuzzy-logic control provides compensation for external errors. The validities of the proposed scheme is evaluated using simulation.

• 한국산학기술학회논문지
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• 제7권5호
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• pp.823-829
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• 2006

본 논문에서는 기존의 PID 제어기의 P 부분만을 퍼지 논리제어기로 대체한 퍼지 P+ID 제어기를 제안하였으며. 제안된 퍼지 P+ID 제어기는 단지 하나의 제어파라미터만을 추가하여 기존 PID 제어기를 조절하기 때문에 쉽게 설계 할 수 있으며, PID 제어기의 구조를 유지함으로서 기존 장치의 하드웨어 부분을 수정할 필요가 없다. 또한, 퍼지 P+ID 제어기는 기존 PID 제어기와 비교해서 충분한 안정성을 보여주며, 구조가 단순하고 계산 량이 적어 제어기의 동조시간을 기존의 퍼지 제어기에 비해서 많이 줄일 수 있는 장점이 있다. 제안된 Fuzzy P+ID 제어기를 BLDC 모터에 적용하여, 시뮬레이션 및 실험을 통하여 본 논문에서 제안한 제어기가 기존의 제어기보다 제어성능이 우수함을 확인하였다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 춘계합동학술대회 논문집
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• pp.121-126
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• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLC[8] for. three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[8].

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.486-489
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• 1996

The objective of this paper is to design a force feedback controller for bilateral control of a master-slave manipulator system. In a bilateral control system, the motion of the master device is followed by the slave one, while the force applied to the slave is reflected on the master. In this paper, a fuzzy logic controllers applied to the system. Using the fuzzy logic controller, the knowledge of the system dynamics is not needed. Simulations and experimental results show the performance of the proposed controller.

• Transactions on Control, Automation and Systems Engineering
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• 제1권2호
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• pp.147-152
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• 1999

It is an interesting area in the field of artifical intelligence to find an analytic model of cooperative structure for multiagent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent agents solving a pursuit problem in a continuous world. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to find the fuzzy logic controller seems to be promising.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.507-512
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• 1998

It is an interesting area in the field of artificial intelligence to and an analytic model of cooperative structure for multi-agent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent co-operative behavior: A modified genetic algorithm was applied to automating the discovery of a fuzzy logic controller jot multi-agents playing a pursuit game. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to and the fuzzy logic controller seems to be promising.