• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.708-712
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• 1991

This paper presents a new adaptive structure with fuzzy logic applied to adaptive controller. In the conventional adaptive control, good performance cannot be expected due to the adaptation gain of gradient algorithm fined as a constant. To change adaptation gain property, fuzzy rules, which are based on the output error and its rate of change, have been established. The proposed fuzzy adaptive law shows fast parameter convergence and improved performance. The fuzzy logic base is added to the conventional adaptive structure and little additional computation time is required.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권9호
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• pp.569-575
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control Performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

• Wind and Structures
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• 제17권4호
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

• Journal of Mechanical Science and Technology
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• 제18권1호
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• pp.1-11
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• 2004

The ionic Polymer Metal Composite (IPMC) is one of the electroactive polymers (EAP) that was shown to have potential application as an actuator It bends by applying a low voltage current (1∼3 V) to its surfaces when containing water In this paper, the basic characteristics and the static ＆ dynamic modeling of IPMC is discussed. In modeling and analysis, the equations of motion, which describe the total dynamics of the system, are driven. To control the position of the IPMC actuator, an adaptive fuzzy algorithm is used. IPMC is a time varying system because the some parameters vary with the passage of time. In this paper, the modeling and control of IPMC is introduced.

• International Journal of Aeronautical and Space Sciences
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• 제17권2호
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• pp.214-221
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• 2016

An attitude estimation algorithm which integrates gyroscope and vision measurements using an adaptive complementary filter is proposed in this paper. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, fuzzy interpolator is applied. For recognizing the dynamic condition of the system and vision measurement fault, the cut-off frequency of the complementary filter is determined adaptively by using the fuzzy logic with designed membership functions. The performance of the proposed algorithm is evaluated by experiments and it is confirmed that proposed algorithm works well in the static or dynamic condition.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권4호
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• pp.228-234
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• 2005

In this paper, we proposed a self tuning adaptive fuzzy sliding control algorithms using gadient 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 automatically updated by consequence part parameter of control rules in order to reduce chattering phenomenon and simultaneously to satisfy 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.238-238
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• 2000

In this paper, to increase the utilization of uranium resources contained in the spent fuel, the spent fuel is reused. for this, the spent fuel is dismantled or spent fuel rod is extracted from the spent fuel assembly. Therefore, to achieve the performance of compacting the spent fuel assembly, we proposed the controller consisting of adaptive and fuzzy with teaming algorithm. In order to show the performance of proposed algorithm compares, we compared the controller with conventional controller in plant.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.103-106
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• 1996

This paper presents an iterative fuzzy learning control scheme which is applicable to a broad class of nonlinear systems. The control scheme achieves system stability and boundedness by using the linear feedback plus adaptive fuzzy controller and achieves precise tracking by using the iterative learning rules. The switching mode control unit is added to the adaptive fuzzy controller in order to compensate for the error that has been inevitably introduced from the fuzzy approximation of the nonlinear part. It also obviates any supervisory control action in the adaptive fuzzy controller which normally requires high gain signal. The learning control algorithm obviates any output derivative terms which are vulnerable to noise.

• 한국통신학회논문지
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• 제21권10호
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• pp.2579-2585
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• 1996

In this paper, a complex fuzzy adaptive decision feedback equalizer(CFADFE) based on the LMS algorithm is proposed. The propoed equalizer is based on the complex fuzzy adaptive equalizer. The CFADFE isconstructed from a set of changeable complex fuzzy IF-THEN rules, where the 'IF' part of the rule is characterized by the state from a set of changealble complex fuzzy IF-THEN rules, where the 'IF' part of the rule is characterized by the state of the desision feedback. the role of decision feedback is to reduce the computational complexity. Computer simulation of the decision feedback. The role of decision feedback is to reduce the computational complexity. Computer simulation shosw that the CFADFE notonly reduces the computational complexity but also improves the performance compared with the conventional complex fuzzy adaptive equalizers. We also show that the adaptation speed is greatly improved by incorporating some linguistic information about the channel into the equalzer. It is applied to M-ary QAM digital communication system with linear and nonlinear complex channel characteristics.

• 한국지능시스템학회논문지
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• 제13권1호
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• pp.85-90
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• 2003

본 논문에서는 퍼지모델을 이용하여 비선형 시스템을 위한 적응 PID 제어기 설계 방법을 제안한다. TSK 퍼지모델을 이용하여 제어 입력의 오차를 예측하고 그 오차로부터 PID제어기의 파라미터를 적응시킨다. TSK 퍼지모델 또한 플랜트의 실제 출력과 모델 출력을 비교하여 모델 파라미터의 적응이 가능하도록 하였다. 제안된 방법으로 비선형의 플랜트에 대한 모호성, 파라미터의 변화 등에 적응할 수 있는 PID 제어기의 설계가 가능하였다. 그리고 몇 개의 비선형 시스템에 대한 시뮬레이션으로 제안된 알고리즘의 유용성도 확인되었다.