• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.712-715
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• 1997

In image jacobian based visual servoing, generally, inverse jacobian should be calculated by complicated coordinate transformations. These are required excessive computation and the singularity of the image jacobian should be considered. This paper presents a visual servoing to control the pose of the robotic manipulator for tracking and grasping 3-D moving object whose pose and motion parameters are unknown. Because the object is in motion tracking and grasping must be done on-line and the controller must have continuous learning ability. In order to estimate parameters of a moving object we use the kalman filter. And for tracking and grasping a moving object we use a fuzzy inference based reinforcement learning algorithm of dynamic recurrent neural networks. Computer simulation results are presented to demonstrate the performance of this visual servoing

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.123.2-123
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• 2001

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the neuro-fuzzy system and the genetic-fuzzy system were used in determining the feeding rate of the coagulant. The fuzzy system is excellently robust in multi-variables and nonlinear problems. Therefore it uses basic algorithm, but it is difficult to construct of the fuzzy parameter such as the rule table and the membership function, Therefore we made the neuro-fuzzy system and the genetic-fuzzy system with the fusion of learning algorithms and compared the performance of the two fuzzy systems. To apply these algorithms, we made the rule table, membership function from the actual operation data of the water treatment plant. We determined optimized feeding rate of coagulant using the fuzzy operation, and also compared ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.110.1-110
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• 2002

We propose a novel neuro-fuzzy system based on an efficient clustering method. It is a very useful method that improves the performance of a fuzzy model with small number of fuzzy rules. The fuzzy clustering methods are studied in the wide range of fuzzy modeling. One of them, the grid partition method has problem of exponentially increasing number of rules when the dimension of input or number of membership function is linearly increased. On the other hand, the Expectation Maximization algorithm is an efficient estimation for unknown parameters of the Gaussian mixture model. Here it is noted that the parameters can be used for fuzzy clustering method. In a fuzzy modeling, it is desired that...

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2120-2122
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• 2002

There are several types of control systems that use fuzzy logic controller as a essential system component. The majority of research work on fuzzy PID controller focuses on the conventional two-input PI or PD type controller. However, fuzzy PID controller design is a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this paper we combined conventional PI type and PD type fuzzy controller and set the initial parameters of this controller from the conventional PID controller gains obtained by Ziegler-Nichols tuning or other coarse tuning methods. After that, by replacing some of these parameters with sing1e neurons and making them to be adjusted by back-propagation learning algorithm we designed a neuro-fuzzy controller which showed good performance characteristics in both computer simulation and actual application.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.296-301
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• 1998

The Rough Set theory suggested by Pawlak in 1982 has been useful in AI, machine learning, knowledge acquisition, knowledge discovery from databases, expert system, inductive reasoning. etc. The main advantages of rough set are that it does not need any preliminary or additional information about data and reduce the superfluous informations. but it is a significant disadvantage in the real application that the inference result form is not the real control value but the divided disjoint interval attribute. In order to overcome this difficulty, we will propose approach in which Rough set theory and Neuro-fuzzy fusion are combined to obtain the optimal rule base from lots of input/output datum. These results are applied to the rule construction for infering the temperatures of refrigerator's specified points.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1892-1896
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• 2005

In this paper, an effective method for selecting significant input variables in building ANFIS (Adaptive Neuro-Fuzzy Inference System) for nonlinear system modeling is proposed. Dominant inputs in a nonlinear system identification process are extracted by evaluating the performance index and they are applied to ANFIS. The availability of our proposed model is verified with the Box and Jenkins gas furnace data. The comparisons with other methods are also given in this paper to show our proposed method is superior to other models.

• 한국정밀공학회지
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• 제15권5호
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• pp.19-27
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• 1998

Color Display Tube (CDT) used in computer monitors, consists of many components. Deflection Yoke(DY) among them supplies the vertical and horizontal magnetic fields so that the spatial trajectories of electron beams are deflected according to the synchronization signals. If the magnetic fields are not correctly formed, there will be color blurring or blooming by a mis-convergence of each beam and the color image on screen may not be clear. Therefore, in the manufacture of DY. its quality is strictly examined to get the desired convergence and the occurred mis-convergence can be cured by sticking ferrite sheets on the inner part of DY. However, because it needs expert's knowledge and experience to find the proper position of the sheet, this article introduces an intelligent controller that the knowledge-base represented by a neuro-fuzzy model is used to find the optimal position of the ferrite sheet for the convergence.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.813-816
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• 1995

The shape and width of fuzzy membership function has an effect on performance of fuzzy controller. In this paper, neuro-fuzzy controller is proposed to improve the control performance of fuzzy controller. It has membership function, that is adapt to plant constant by using trained neural network. This neural network has been trained with back propagation algorithm. To show the effectiveness of proposed neuro-fuzzy controller with adaptive membership function, it is applied to plant (dead time + 1st order) with various plant constant.

• 한국산학기술학회논문지
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• 제7권5호
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• pp.830-836
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• 2006

본 논문에서는 기존의 PID 제어기와 퍼지 제어기의 특성을 공통으로 갖는 새로운 형태의 신경회로망 기반 자동 동조 뉴로-퍼지 PID제어기를 제안하였다. 제안된 제어기는 퍼지의 선형성을 이용하여 퍼지 PID 제어기의 퍼지 연산부를 간략화 시키고 일반 PID 제어기와 유사한 입출력 특성을 갖도록 하였으며 비선형 성분 보상을 위하여 제어기 출력에 가장 큰 영향을 미치는 출력측 스케일 계수를 단일 신경 회로망 구조로 변경하고 PID 제어기 구조를 유지하게 하였다. 또한 단일 신경 회로망 구조를 이용함으로써 신경회로망의 초기 연결강도와 계산량에 대한 문제점을 해결하고 오차의 부호 정보에 따라 학습계수를 변화시키는 가변 학습계수 역전파 알고리즘을 사용하여 오버 슈트가 작으면서도 빠른 수렴 속도를 갖도록 하였다. 제안된 제어기를 비선형성이 강한 시스템으로 알려진 자기 부양(magnetic levitation) 시스템에 실제 적용하여 본 논문에서 제안한 제어기의 우수한 성능을 확인하였다.