• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.752-758
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• 2004

In this paper, we proposed an adaptivefuzzy sliding control algorithm using gradient descent method to reduce chattering phenomenon which is viewed in variable control system. In design of FLC, fuzzy control rules are obtained from expert's experience and intuition and it is very difficult to obtain them. We proposed an adaptive algorithm which is updated by consequence part parameter of control rules in order to reduce chattering phenomenon and simultaneously to satistfy the sliding mode condition. The proposed algorithm has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties in the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum system. The results show that both alleviation of chattering and performance are achieved.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.270-275
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• 2005

This paper presents a fuzzy model-based adaptive approach for synchronization of chaotic systems which consist of the drive and response systems. Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, we design the response system that estimates the parameters of the drive system by adaptive strategy. The adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. In addition, the controller in the response system contains two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples, including Doffing oscillator and Lorenz attractor, are given to demonstrate the validity of the proposed adaptive synchronization approach.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.309-312
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• 2001

본 연구에서는 상류부 교차로에서 발생하는 교차로 막힘 현상으로 인해 진행방향의 녹색시간의 손실이라는 장애가 발생하게되는 상황을 고려하기 위해 진행차선의 정체도를 도입하여 교통 혼잡상황에 적절히 대응할 수 있는 퍼지 교통신호 제어기를 제안한다. 먼저 입출력 공간을 균등 분할한 퍼지 교통신호 제어기를 구성하고, 소속함수 수정알고리즘에 의해 제어기를 수정한다. 실험을 통해 고정식 제어기, 균등 분할한 제어기와 수정된 제어기의 성능을 교차로 지체시간, 진입율과 통과율 면에서 비교하였다. 실험 결과는 수정된 제어기가 다른 제어기들에 비해 향상된 성능을 보여주었다.

• The Journal of Information Technology
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• v.5 no.4
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• pp.57-64
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• 2002

The system that is thermal test system for elements has been controlled generally by PID algorithm because of its characteristic. There is not a mathematical model for the system. So the system that is use the PID controller is not properly operated. To solve this problem, we propose a fuzzy algorithm that parameters and rule base is selected by self-searched algorithm for each system. The input fuzzy membership function is adapted based on the set stable range. Output membership function is nearly fixed but some parameter is adjustable. The rule base is changed under basis on the system response. The output value computed through inference and defuzzification is mapped into a value that is proper for the system operation. Through this regulation, it will be possible to prevent the temperature of system to go into the unstable temperature.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.395-400
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• 2000

In this paper, we propose a new neuro-fuzzy controller based on reinforcement learning. The proposed system is composed of neuro-fuzzy controller which decides the behaviors of an agent, and dynamic recurrent neural networks(DRNNs) which criticise the result of the behaviors. Neuro-fuzzy controller is learned by reinforcement learning. Also, DRNNs are evolved by genetic algorithms and make internal reinforcement signal based on external reinforcement signal from environments and internal states. This output(internal reinforcement signal) is used as a teaching signal of neuro-fuzzy controller and keeps the controller on learning. The proposed system will be applied to controller optimization and adaptation with unknown environment. In order to verifY the effectiveness of the proposed system, it is applied to collision avoidance of an autonomous mobile robot on computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.584-588
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• 1991

This paper presents a fuzzy adaptive controller which can improve the control policy automatically. Adaptation is achieved by the addition of on-line identification of the fuzzy inverse model using input-output data pairs of the process. Starting with an initial crude control rule, the adaptive controller matches the model to the process to self-tune the controller. The control algorithm needs much less memory of computer than other SOC algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6034-6039
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• 2012

In this paper, this may appear to exacerbate it met slopes of the mobile robot moves to overcome this by driving can occur if the mobile robot system has its own sleep problems driving progress in until you hit the target and solvedriving straight driving safer model for adaptive fuzzy control method of mobile robot based control algorithm is proposed. First, we propose a model based adaptive fuzzy controller, if possible, the dynamics model of the mobile robot, including model-based controller is designed to determine if you can check the condition of the mobile robot climbing and driving the mobile robot to overcome the slope and the to overcome driving control. Enough considering the ground friction forces and ensure the stability of the mobile robot system and the disturbance compensation, etc. In this case, the controller design will be possible. In addition, the nonlinear model, the dynamic characteristics of the mobile robot control method of adaptive fuzzy control techniques in the design that you want to fully reflect Non-holonomic system of mobile robots and solve sleep problems, and will be useful enough, it was verified through computer simulations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.197-200
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• 2017

퍼지 논리를 기반으로 한 적응형 스트리밍 기법인 FDASH 적응 알고리즘은 빠르게 변하는 네트워크 상황에서 우수한 콘텐츠의 화질을 보장하면서 끊김 없는 서비스를 제공하는 특성을 보이지만 비디오의 화질이 자주 변하기 때문에 최고의 사용자 체감 품질 (QoE: Quality of Experience)을 제공하지 못 할 수도 있다. 본 논문에서는 제한된 버퍼 크기를 가지고 동일한 콘텐츠의 화질을 보장하면서도 비디오 화질의 변화 횟수를 줄여서 최적의 QoE를 제공할 수 있도록 하는 변환된 퍼지 논리 기반의 DASH 적응 알고리즘을 제안하고자 한다. 제안된 방식은 우선 퍼지 논리 제어부(FLC : Fuzzy Logic Controller)의 수정을 통하여 다음 세그먼트의 비트율에 대해 최적의 판단을 하도록 하였고, 세그먼트 비트율 필터링 모듈 (SBFM: Segment Bitrate Filtering Module)을 추가하여 비디오 화질의 변화 횟수가 최소화 될 수 있도록 하였으며, 스트리밍 서비스 시작 시 SBFM에 의해 일정시간 저화질의 비디오를 시청해야 하는 상황을 막기 위한 Start Mechanism을 추가하였고, 마지막으로 버퍼의 오버플로우를 방지하기 위해 Sleeping Mechanism을 추가하였다. NS-3를 이용한 네트워크 모의실험 결과를 통해 제안된 방식이 FDASH 방식에 비하여 제한된 버퍼크기 상황 하에서도 오버플로우가 발생하지 않으며 점대점(Point to Point) 상황에서는 거의 동일 화질 성능을 보이면서도 비디오 화질 변화 횟수를 50% 이상 줄일 수 있음과 일반 Wifi환경에서는 오히려 17.8%정도 더 뛰어난 비디오 화질 성능을 보이면서 비디오 화질변화 횟수 측면에서는 53.1%정도 줄일 수 있음을 보여준다.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.72-81
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• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.95-103
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• 1995

Recently the trends toward reduction in size of industrial products have increased the application of micro drilling. But micro drilling has still much difficulty so that the needs for active control which give adaptation to controller are expanding. In this paper initial cutting condition was determined for some sorkpieces by experiment and GA-based Fuzzy controller was devised by genetic algorithms and fuzzy inference. The fuzzy inference has been applied to the various prob- lems. However the determination of the membership function is one of the difficult problem. So we introduce a genetic algorithms and propose a self-tuning method of fuzzy membership function. Based on this intelligent control, automation of micro drilling was carried out like the cutting process of skilled machinist.