• 한국지능시스템학회논문지
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• 제6권2호
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• pp.97-105
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• 1996

본 논문에서는 퍼지모델을 이용한 비선형 공정의 적응모델예측제어가 제안된다. 모델예측제어의 저긍구조는 순환 퍼지모델링을 통해 구현된다. 사용된 퍼지모델의 후건부가 입, 출력 변수의 선형식이기 때문에, 전체 공정의 모델을 구하고 이를 이용하여 미래 공정출력을 구한 후 비용함수를 최로로하는 제어법칙은 일반형 예측제어(GPC)와 같은 형태가 된다. 제안된 적응 퍼지모델 예측제어는 퍼지모델이 가지는 본래적인 비선형성으로 인해 비선형공정을 우수한 성능으로 제어한다. 공정제어입력의 변화량을 출력값으로 하는 적응 퍼지모델 예측제어(AFMPC)인 경우, 상수의 기준입력에 대해 정상상태가 없고 매우 우수한 성능을 보인다. 제안된 제어구조의 특성 및 성은 비선형 공정의 모의 실험에 의해 검증한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.12-18
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• 2010

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2005년도 학술대회 논문집
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network 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 nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 한국정보통신학회논문지
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• 제5권4호
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• pp.730-737
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• 2001

본 논문에서는 적응 뉴로-퍼지 제어기를 이용하여 비선형 복합시스템 모델의 안정화 제어 방법에 적용한다. 제안된 적응 뉴로-퍼지 제어기는 언어적 퍼지추론, 프로세스의 입출력 데이터를 이용하는 신경회로망, 최적이론 등이 포함된 인공지능을 시스템구조와 파라메터 검증에 필요한 도구로 이용한다. 그 결과 제안된 방법이 이전에 연구되었던 다른 방법보다 아주 높은 인공지능 모델을 제시하였다.

• Journal of Advanced Marine Engineering and Technology
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• 제19권3호
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• pp.77-83
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• 1995

Recently, in the field of industrial servo-systems, several methods have been proposed for tracking the reference input fastly and finely without overshoot. These methods, however, are established under hypothesis that structure and parameters of the plant are known accurately and they are time invariant. In practice, it is difficult to obtain the values of plant's parameters accurately and usually plants change with time and operation conditions. In this paper a method to construct the nominal model tracking adaptive control system is proposed. The system is composed of the nomial model which produces a ideal response and the model tracking system with the fuzzy adaptive controller. If the actual plant is equal to the controlled object in the nominal model, the output of the plant is the same as that of the nominal model and the fuzzy adaptive controller becomes idle. However, when the plant changes, the fuzzy adaptive controller of the tracking system operates in order for the output of the plant to track the ideal response. Through the computer simulations under various conditions, it is confirmed that the proposed model tracking system is very effective.

• 한국지능시스템학회논문지
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• 제15권2호
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• pp.270-275
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• 2005

본 논문은 퍼지 모델을 이용한 불확실한 카오스 시스템의 적응 동기화 기법을 제안한다. 카오스 동기화 시스템은 마스터 시스템과 슬레이브 시스템으로 구성되며 각각의 시스템은 Takagi-Sugeno (T-S) 퍼지 모델을 통해 표현된다. 마스터 시스템은 파라미터가 미리 알려지지 않은 불확실한 모델로 가정되므로 불확실한 파라미터를 추정하기 위해 적응 기법을 적용하여 슬레이브 시스템을 설계한다. 동기화 오차 시스템을 안정화하고 불확실한 파라미터를 추정하는 적응 규칙을 이용한 제어기를 설계하며 Lyapunov 이론을 통해 안정도를 해석한다. 제안된 퍼지 적응 동기화 기법의 효과를 확인하기 위해서 Duffing 시스템과 Lorenz 시스템에 대해 모의 실험을 수행한다.

• Journal of Advanced Marine Engineering and Technology
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• 제30권7호
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• pp.791-799
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• 2006

The aim of this paper is to design an adaptive speed control system of a marine diesel engine by fusion of hard computing based proportional integral derivative (PID) control and soft computing based fuzzy control methods. The model of a marine diesel engine is considered as a typical non oscillatory second order system. When its model and the actual marine diesel engine ate not matched, it is hard to control the speed of the marine diesel engine. Therefore, this paper proposes two methods in order to obtain the speed control characteristics of a marine diesel engine. One is an efficient method to determine the PID control parameters of the nominal model of a marine diesel engine. Second is a reference adaptive speed control method that uses a fuzzy controller and derivative operator for tracking the nominal model of the marine diesel engine. It was found that the proposed PID parameters adjustment method is better than the Ziegler & Nichols' method, and that a model reference adaptive control is superior to using only PID controller. The improved control method proposed here, could be applied to other systems when a model of a system does not match the actual system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• Journal of Advanced Marine Engineering and Technology
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• 제24권2호
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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.

• 한국지능시스템학회논문지
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• 제11권8호
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• pp.771-775
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• 2001

본 논문에서는 유도탄 오토파일롯을 제어하기 위해 T-S 퍼지 모델링을 한 다음 병렬분상이론을 적용하여 적응 퍼지 제어기를 설계한다. 추가적으로 제어기의 파라미터는 기준모델과 출력간의 에러, 스테이트, 기준입력 신호를 이용하여 실시간 업데이트되며, 원 플랜트에 대해 regulation 제어가 성공적으로 해결함을 미사일 모델에 적용한 모의 실험 결과로부터 보인다.