• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.512-515
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• 1997

In this paper the fuzzy-genetic controller for path-tracking of WMRs is proposed. Fuzzy controller is implemented to adaptive adjust the crossover rate and mutation rate, and genetic algorithm is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-genetic controller is effective.

• Journal of Mechanical Science and Technology
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• 제18권4호
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• pp.535-549
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• 2004

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator(FAPC) to get a good control performance and reduce the chatter, The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network(FAN) The weighting parameters of the compensate. are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.

• Transactions on Control, Automation and Systems Engineering
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• 제3권2호
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• pp.117-123
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• 2001

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.921-923
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• 1999

In adaptive fuzzy control, fuzzy systems are used to approximate the unknown plant nonlinearities. However, because of the approximating error introduced into the feedback loop, it is difficult to guarantee the stability of the adaptive control algorithm. This paper presents a robust control algorithm against the reconstruction error and uniform boundedness of the all signals is estabilished in the Lyapunov sense.

• 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.

• 한국시뮬레이션학회논문지
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• 제14권4호
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• pp.95-106
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• 2005

Recent advances in wireless sensor networks have led to many routing protocols designed for energy-efficiency in wireless sensor networks. Despite that many routing protocols have been proposed in wireless sensor networks, a single routing protocol cannot be energy-efficient if the environment of the sensor network varies. This paper presents a fuzzy logic based Adaptive Routing (FAR) algorithm that provides energy-efficiency by dynamically changing protocols installed at the sensor nodes. The algorithm changes protocols based on the output of the fuzzy logic which is the fitness level of the protocols for the environment. A simulation is performed to show the usefulness of the proposed algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.309-314
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• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

• 대한전기학회논문지:전력기술부문A
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• 제49권5호
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• pp.205-211
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• 2000

This paper presents an optimal design method for fuzzy logic controller (FLC) of HVDC using an Adaptive Evolutionary Algorithm(AEA). We have proposed the AEA which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary algorithms. The AEA is used for tuning fuzzy membership functions and scaling constants. Simulation results show that disturbances are well damped and the dynamic performances of FLC have better responses than those of PD controller when AC system load changes suddenly.

• 지능정보연구
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• 제8권2호
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• pp.189-203
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• 2002

이 논문에서 우리는 유전알고리즘의 적응적 연산자에 대한 수행도를 비교한다. 이러한 적응적 연산자를 위해서, 유전알고리즘의 교차변이와 돌연변이 연산자가 고려되어 지며, 이 논문에서 개발된 하나의 퍼지로직 제어기와 기존연구에서 사용된 두개의 휴리스틱 기법이 제시되어진다. 이러한 퍼지로직 제어기와 두개의 기존 휴리스틱 기법들은 유전 탐색과정 동안에 그 연산자의 비율들을 적응적으로 조절한다. 이 논문에서 제시된 모든 알고리즘들은 수치예제에서 분석되어 지며, 결론적으로 이들 알고리즘 중에서 최적의 알고리즘이 추천된다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.624-627
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• 2000

An adaptive fuzzy sliding-mode control system which combines the merits of sliding-mode control the fuzzy inference mechanism and the adaptive algorithm is proposed. A fuzzy sliding-mode controller is investigated in which a simple fuzzy inference mechamism is used to estimate the upper bound of uncertainties., The fuzzy inference mechanism with centre adaptation of membership functions is investigated to estimate the optimal bound of uncertainties.