• 제어로봇시스템학회논문지
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• 제9권5호
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• pp.379-383
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• 2003

Parallel type fuzzy controller is designed by using a hybrid connected type fuzzy-PID controller and a model reference fuzzy controller. The first controller, consists of a fuzzy-PI and a fuzzy-PD making a hybrid type fuzzy-PID controller, plays a role as firstly reaching stable responses and secondly overcoming disturbance in plants. The second controller, model reference fuzzy controller, plays a role as reaching faster responses than other controllers. We have confirmed that the controller produces rapid and stable responses and overcomes disturbance by using parallel type fuzzy controller in a DC motor application.

• 한국산업융합학회 논문집
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• 제19권3호
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• pp.101-108
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• 2016

I propose a hybrid fuzzy controller with a look-up table to improve the performance of the FARMA(Fuzzy Auto-regressive Moving Average) fuzzy controller. The hybrid structure of the proposed method is composed of a fuzzy controller with a look-up table of the PD type and the FARMA fuzzy controller. The proposed method improves poor performance due to the lack of I/O data to calculate predictive output and shows robust performance over the FARMA fuzzy controller when a incorrect Dmax value is selected by trial and error. I executed some computer simulations on the regulation problem of an inverted pendulum system and compared the results with those of the FARMA fuzzy controller.

• 전자공학회논문지CI
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• 제37권5호
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• pp.19-28
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• 2000

구륜 이동 로봇(Wheeled Mobile Robot)은 제어기 설계에 있어 Nonholonomic Constraints등에 의해 많은 어려움을 지닌다. 본 논문에서는 구륜 이동 로봇의 제어를 위해 PD와 퍼지 시스템이 결합된 제어기가 설계되며, 유전알고리즘에 기초되어 최적 퍼지시스템이 형성된다. 시스템의 최적화 과정은 독립적으로 수행되는 여러 단계들로 이루어지며, 각 단계마다 다른 형식의 알고리즘이 적용되며 효율적 탐색을 위해 Niche알고리즘 및 면역 알고리즘이 결합되어 적용된다. 각 출력용어집합은 최적의 원소들을 얻기 위해 수행되는 탐색에 의해 그 구성이 변화되며, 변화된 출력용어집합의 구성 원소와 관계된 규칙기반이 동시에 조절된다. 출력용어집합의 추가된 원소들 및 조절된 규칙에 대한 적합성이 평가되고 제어 성능의 향상에 기여하지 못한 부분들은 제거된다. 출력변수의 용어집합 및 규칙에 대한 반복적 조절 과정이 완료된 후, 입력 소속함수들에 대한 조정이 제약조건을 가지고 수행되며, 진화연산에 의한 출력소속함수들에 대한 조정이 수행된다.

• 제어로봇시스템학회논문지
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• 제13권7호
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• pp.671-676
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• 2007

In this paper, a novel neuro-fuzzy controller is presented. The generic fuzzy controller is compensated by a neural network controller so that an overall control structure forms a neuro-fuzzy controller. The proposed neuro-fuzzy controller solves the difficulty of selecting optimal fuzzy rules by providing the similar effect of modifying fuzzy rules simply by changing crisp input values. The performance of the proposed controller is tested by controlling humanoid robot arms. The humanoid robot arm is analyzed and implemented. Experimental studies have shown that the performance of the proposed controller is better than that of a PID controller and of a generic fuzzy PD controller.

• 한국지능시스템학회논문지
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• 제25권1호
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• pp.1-6
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• 2015

본 논문에서는 한 바퀴 이동로봇의 균형제어를 위해 퍼지방법을 사용하였다. PD제어를 사용하여 균형을 유지하는 한 바퀴 로봇은 시간이 지남에 따라 플라이휠이 한 방향으로 기울어지게 되고 결국에는 균형이 무너지는 현상이 발생한다. 선행연구에서는 이를 해결하기 위해 게인 스케줄링 방법을 사용하였다. 본 논문에서는 퍼지방법을 사용하여 균형 각도를 보상하므로 균형 제어 성능을 높이고자 하였다. 퍼지제어를 통해 desired offset의 각도를 보상하므로 김벌이 한쪽으로 흘러 넘어지는 현상을 보완하였다. 한 바퀴 구동 이동로봇의 균형제어 실험을 통하여 제안하는 제어방식의 성능을 검증하였다.

• 대한전기학회논문지:전력기술부문A
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• 제51권4호
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• pp.188-195
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• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLS[10] for three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[10].

• 한국지능시스템학회논문지
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• 제5권3호
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• pp.36-51
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• 1995

An approach to robot hybrid position/force control, which allows force manipulations to be realized without overshoot and overdamping while in the presence of unknown environment, is given in this paper. The manin idea is to used dynamic compensation for known robot parts and fuzzy compensation for unknown environment so as to improve system performance. The fuzzy compensation is implemented by using rule based fuzzy approach to identify the unknown environment. The establishment of proposed control system consists of following two stages. First, similar to the resovled acceleration control method, dynamic compensation and PD control based on known robot dynamics, kinematics and estimated environment stiffness is introduced. To avoid overshoot the whole control system is constructed with overdamping. In the second stage, the unknown environment stiffness is identified by using fuzzy reasoning, where the fuzzy compensation rules are obtained priori as the expression of the relationship betweenenvironment stiffness and system. Based on the simulation result, comparison between cases with or without fuzzy identifications are given, which illustrate the improvement achieced.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.416-419
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• 2001

In this paper a new approach to estimate scaling factors of fuzzy controllers such as the fuzzy PID controller and the fuzzy PD controller is presented. The performance of the fuzzy controller is sensitive to the variety of scaling factors[1]. The desist procedure dwells on the use of evolutionary computing(a genetic algorithm) and estimation algorithm for dynamic systems (the inverted pendulum). The tuning of the scaling factors of the fuzzy controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy controller by means of two types of estimation algorithms such as Neuro-Fuzzy model, and regression polynomial [7]. This method can be applied to the nonlinear system as the inverted pendulum. Numerical studies are presented and a detailed comparative analysis is also included.

• 제어로봇시스템학회논문지
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• 제7권7호
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• pp.622-631
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• 2001

This paper presents a fuzzy modified PID controller that uses linear fuzzy inference method. In this structure, the proportional and derivative gains vary with the output of the system under control. 2-input PD type fuzzy controller is designed to obtain the varying gains. The proposed fuzzy PID structure maintains the same performance as the same performance as the general-purpose linear PID controller, and enhances the tracking performance over a wide range of input. Numerical simulations and experimental results show the effectiveness of the fuzzy PID controller in comparison with the conventional PID controller.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2005년도 하계학술대회 논문집 Vol.6
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• pp.566-567
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• 2005

Until now variable pattern classification methods have been introduced. So, variable methods in PD source classification were applied. NN(neural network) the most used scheme as a PD(partial discharge) source classification. But in recent year another method were developed. These methods is present superior to NN in the field of image and signal process function of classification. In this paper, it is show classification result in PD source using three methods; that is, BP(back-propagation), ANFIS(adaptive neuro-fuzzy inference system), PCA-LDA(principle component analysis-linear discriminant analysis).