• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
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• pp.328-332
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• 2007

본연구에서는 센서의 융합을 통하여 환경을 인식하며, 주변환경에 대한 지식을 갱신, 학습할수 있는 방법론을 연구하며, 동적인 장애물의 감지및 움직임 예측에 기반한 지능적 회피 알고리즘과 AHP를 이용한 Navigation Strategy수정과 이동 로봇 스스로 최적의 결과를 낼수 있게 개선 시키는 알고리즘을 구현한다. 그와 더불어 AHP를 이용하여 Navigation Performance를 최대로 높일 수 있는 방향을로 진화시키는 알고리즘을 구현한다. 또한 부여된 임무수행을 위한 목표물 추적을 위한 비전 시스템에서의 대상체 추출및 인식 알고리즘을 개발하며 인간뇌의 환경인식 체계와 유사한 방식의 Map building기법을 연구한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.861-865
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• 1991

In general, the fusion of neural network and fuzzy logic theory is based on the fact that neural network and fuzzy logic theory have the common properties that 1) the activation function of a neuron is similar to the membership function of fuzzy variable, and 2) the functions of summation and products of neural network are similar to the Max-Min operator of fuzzy logics. In this paper, a fuzzy-neural network will be proposed and a force controller of the robot gripper, utilizing the fuzzy-neural network, will be presented. The effectiveness of the proposed strategy will be demonstrated by computer simulation.

• 한국산업융합학회 논문집
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• 제19권4호
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• pp.193-197
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• 2016

There are many hoppers in the actual industrial plants such as grain hoppers in agricultural companies, surge hoppers in steel companies, and so on. In general, they are time-delay systems so that their level control is not easy. Thus, they have been manually controlled to avoid the overflow and empty out. In this paper, we proposed Fuzzy PID controller for level control of hopper systems. To show the effectiveness of the proposed scheme, some simulation results are given.

• 한국산업융합학회 논문집
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• 제6권1호
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• pp.37-42
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• 2003

In this paper a self-organizing fuzzy system with rule pruning is proposed. A conventional self-organizing fuzzy system having only rule generation has a drawback in generating many slightly different rules from the existing rules which results in increased computation time and slowly learning. The proposed self-organizing fuzzy system generates fuzzy rules based on input-output data and prunes redundant rules which are caused by parameter training. The proposed system has a simple structure but performs almost equivalent function to the conventional self-organizing fuzzy system. Also, this system has better learning speed than the conventional system. Simulation results on several numerical examples demonstrate the performance of the proposed system.

• 한국산업융합학회 논문집
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• 제3권2호
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• pp.123-130
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• 2000

Fuzzy control is proposed to regulate cutting force in turning operations under varying cutting conditions. The traditional linear controllers based on crisp mathematical model cannot effectively control cutting force becasue of the nonlinear dynamics of turning operations. The proposed fuzzy controller is based on operator experience and expert knowledge. The membership functions for the inputs and the output of the controller are designed. Cutting force is regulated by adjusting feedrate according to the variation of cutting conditions. The performance of the proposed controller is evaluated by experiments. The results of experiments show that the proposed fuzzy controller has a good cutting force regulation over a wide range of cutting conditions.

• 한국산업융합학회 논문집
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• 제2권1호
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• pp.61-67
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• 1999

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.

• 한국산업융합학회 논문집
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• 제17권2호
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• pp.69-76
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• 2014

This paper proposes a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, 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 on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 한국산업융합학회 논문집
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• 제6권4호
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• pp.393-399
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• 2003

In this paper, a self-organizing fuzzy logic controller with hybrid structure is proposed. The structure of the proposed method is composed of a basic fuzzy logic controller and the FARMA SOC(Fuzzy Autoregressive Moving Average Self-organizing Controller). The self-organizing cntroller with hybrid structure has advantage over the FARMA controller as follows. The proposed controller improves poor performance due to the lack of I/O data to calculate predictive output. I executed some computer simulations on the regulation problem of an inverted pendulum system and compared the results of the proposed method with those of the FARMA SOC method.

• 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.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2001년도 하계 학술대회 논문집(KISPS SUMMER CONFERENCE 2001
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• pp.105-108
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• 2001

In this paper, we proposed a variable structure controller with fuzzy PI-쇼pe reaching law. we fuzzified as inputs to fuzzy system Rf(representative point's orthogonal distance(rd) to switching surface and RP's distance(r) to the origin of the 2-dimensional space whose coordinates are the error and the error rate. The increments of the coefficients $k_{p}$ and $k_{i}$, of the reaching law are calculated appropriate by the simplified Mamdanl inference. The proposed fuzzy PI-type reaching law makes it reduce the chattering and has no need to tune the PI parameters of reaching law. The effectiveness of the proposed fuzzy PI-type reaching law is shown by the simulation results of the control of a Ball-balance System.