• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 추계학술대회 논문집
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• pp.70-75
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• 1997

This paper proposes a new approach to the design of fuzzy-neuro control for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• 전기학회논문지P
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• 제51권2호
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• pp.99-104
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• 2002

In this paper, we construct a hybrid intelligent controller based on a fusion scheme of GA(Genetic Algorithm) and FCM(Fuzzy C-Means) clustering-based ANFIS(Adaptive Neuro-Fuzzy Inference System). In the structure identification, a set of fuzzy rules are generated for a given criterion by FCM clustering algorithm. In the parameter identification, premise parameters are optimally searched by adaptive GA. On the other hand, consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. Finally, we applied the proposed method to the truck backer-upper control and obtained a better performance than previous works.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.161-164
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• 2004

The yo-yo system has been introduced as an interesting plant to demonstrate the effectiveness of intelligent controllers. Having nonlinear and asymmetric characteristics, the yo-yo plant requires a controller quite different from conventional controllers such as PID. In this paper is presented an adaptive method of controlling the yo-yo system. Fuzzy logic controller based on human expertise is referred at first. Then, an adaptive fuzzy controller which has adaptation features against the variation of plant parameters is proposed. Finally, experimental results are presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.693-695
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• 1998

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. It is very important to decide parameters of IF-THEN rules. Because fuzzy controller can make more adequate force to the plant by means of parameter optimization, which is accomplished by learning procedure. In this paper, we apply fuzzy controller designed to the inverted pendulum.

• Wind and Structures
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• 제22권6호
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• pp.635-662
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• 2016

This paper deals with the comparison of different control strategies for the Induction generators in wind energy conversion system. Mainly, two types of induction machines, Self excited induction generator (SEIG) and doubly Fed Induction generators (DFIG) are studied. The different control strategies for SEIG and DFIG are compared. For SEIG, Electronic load Controller mechanism, Static Compensator based voltage regulator are studied. For DFIG the main control strategy namely vector control, direct torque control and direct power control are implemented. Apart from these control strategies for both SEIG and DFIG to improve the performance, the ANFIS based controller is introduced in both STATCOM and DTC methods. These control methods are simulated using MATLAB/SIMULINK and performances are analyzed and compared.

• 조명전기설비학회논문지
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• 제12권1호
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• pp.78-84
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• 1998

발전소나 화학플랜트의 급수 및 순환수 계통은 물론 건물의 공조설비 유체계통, 정수 또는 폐수설비에서의 원수유량 및 약품 주입제어 등과 같은 유체계통은 대부분 밸브와 동력 펌프를 이용해 유량 제어를 한다. 따라서 이들 유체계통의 제어방법 및 정밀도에 따라 그 시스템의 성능 및 에너지 절약에 많은 영향을 미치게 된다. 지금까지 이들 계통은 주로 PI제어기를 이용하였으나 이 제어 방법으로는 루프간의 간섭현상으로 정밀하게 유량부하를 제어 할수 없는 문제점이 있다. 본 논문에서는 유체계통 동력부하 설비를 다변수로 구성하여 2-자유 PID제어를 적용하고 신경망을 이용해 튜우닝함으로서 효과적으로 정밀하게 제어 할수 있는 방법을 제시하고 시뮬레이션을 통해 그 방법의 우수성을 입증하였다. 시뮬레이션 결과 설정치를 잘 추종하고 외란에 대해서는 강인성을 갖는 것으로 나타났다.

• 한국통신학회논문지
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• 제25권4B호
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• pp.683-691
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• 2000

본 논문에서는 정확한 연결 설정을 결정하기 위해 Hopfield 신경회로망을 이용한 셀 다중화기와 역전파 신경회로망을 이용한 대역폭 예측기를 제안하였다. 다중화된 대역폭에서 망의 이용률을 극대화시키고 이용자 트랙픽의 QoS를 만족시키는 최소 대역폭이 새로 고안한 역전파 신경회로망 대역폭 예측기를 통하여 예측되어진다. 연결 수락 제어기는 예측된 대역폭과 망내의 사용 가능한 대역폭을 비교하여 연결 수락 여부를 판단한다. 연결이 설정된 사용자 소스를 감시하며 계약 위반시 적절한 조치를 취하는 퍼지 논리 제어 트래픽 감시 방법과 멀티미디어 트래픽의 주된 특성인 버스트 제어를 통한 망의 효율을 증가시키는 분석적 트래픽 형태 제어 방법을 제시한다. 제안된 트래픽 제어기는 성능이 우수하다고 평가된 기존의 제어기들과 성능 평가를 하였으며, 시뮬레이션 결과는 기존의 제어기보다 성능이 우수함을 보여주었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.227-230
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• 1993

Recently, the development of novel control methodology enables us to improve the performance of AC-machine drives by using pulse width modulation (PWM) technique. Usually, the dynamic characteristic of induction motor (IM) has been represented by the 5-th order nonlinear differential equation. This dynamics, however, can be reduced to 3-rd order dynamics by applying direct control of IM input current. This methodology concludes that it is much easier to control IM by means of the field-oriented methods employing the current controller. Therefore a precise current control is crucial to achieve a high control performance both in dynamic and steady state operations. This paper presents an adaptive fuzzy current controller with artificial neural network (ANN) for field-oriented controlled IM. This new control structure is able to adaptively minimize a current ripple while maintaining constant switching frequency. Especially the proposed controller employs neuro-computing philosophy as well as adaptive learning pattern recognizing principles with respect to variations of the system parameters. The proposed approach is applied to the IM drive system, and its performance is tested through various simulations. Simulation results show that the proposed system, compared among several known classical methods, has a superb performance.

• 한국정보통신학회논문지
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• 제4권3호
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• pp.619-625
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• 2000

많은 제어기법들이 이산시간영역제어계의 제어성능을 개선시키기 위해서 제안되고 있다. 위치제어계에서 일반적으로 제어기의 출력이 제어대상의 입력으로 사용된다. 그러나 불필요한 잡음이 제어기의 출력에 포함된다. 그러므로 이러한 잡음을 제거하기 위해서 전치보상기를 사용할 필요가 있다. 본 논문에서 전치보상기를 갖는 확대 I-PD 제어계를 구성하였고 이 전치보상기와 I-PD 제어기를 신경망으로 설계하였다. 그리고 전치보상기와 I-PD제어기의 계수들이 계의 응답특성들이 어떤 조건 하에서 변할 때 계의 희망하는 응답이 되도록 자동적으로 조정되도록 하였다.