• Journal of the Korean Institute of Intelligent Systems
• /
• v.6 no.4
• /
• pp.3-9
• /
• 1996

본 논문에서는 Boole 함수를 기호 다치 논리 함수로 확장하여 법-M(Modulus-M)의 수체계를 기본으로 하는 기호 다치 논리 함수에 대한 MacLaurin 전개의 구조적 성질을 분석한다. 그리고 기호 다치 변수의 상태 변화에 따라 이에 사상된 퍼지 규칙을 자동 생성할 수 있는 기법을 제안한다. 또한 이러한 이론과 성질을 기존의 퍼지 추론 기능과 결합하여 동적인 상태 변화에 적응할 수 있는 퍼지 추론 시스템 설계방법을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.11a
• /
• pp.243-246
• /
• 2006

본 논문에서는 모발내에 있는 약 30여가지의 생체 미네랄과 8가지의 중금속 정보 분석을 통해 생체내에 양양상태의 과잉, 결핍 및 불균형 상태를 평가하고, 그 결과가 현재 생체에 미치는 영향을 예측하여, 건강을 유지하는 방향을 제시할 수 있는 의료용 지능적 의학진단 시스템을 구축하였다. 이 논문에서는 생체내 미네랄 정보를 다중 퍼지규칙베이스 시스템으로 구축함으로써 환자에게 보다 효율적으로 치료와 예방방법을 제시할 수 있는 의학진단시스템을 구축하였다.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1172-1177
• /
• 2003

In this paper, we propose an obstacle avoidance technique in trajectory tracking of nonholonomic wheeled mobile robots. Input-output linearized backstepping controller is used in trajectory tracking, and repulsive type control input for obstacle avoidance is added to it. The added input is generated by fuzzy logic. And we do not add the two inputs directly but combine them via fuzzy logic, which determines the ratings of each input. Some simulations are performed to show that with the proposed algorithm, the mobile robot can track its reference trajectory even if there are multiple obstacles on the trajectory of robot.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.390-394
• /
• 1994

A elevator group supervisory system is designed to perform efficient operation of multiple elevators, and its basic function is to assign an appropriate elevator to a given hall-cell. In this paper, in order to improve elevator group control performance, we propose a new dispatching system which includes fuzzy multi-attribute decision making(MADM). In most cases, the purpose of group control is to maximize control goals as much as possible. Unfortunately, the decision of optimal elevator to a given hall cell is made with very uncertain information of the system, and some of control goals are related each other. The uncertainty is mainly resulted from car calls generated by serving hall calls. A fuzzy MADM algorithm is proposed to deal with these problems to improve system performance.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.67.3-67
• /
• 2001

A fuzzy control strategy is described which is utilized to control the joint angle and tip deflection in single flexible manipulator. In this paper, an existing model for a single flexible manipulator is used f3r the initial development of an FLC. One FLC is designed to govern the joint angle of the manipulator as it is rotated from one position to another, and a second FLC is designed to attenuate the tip deflection which result from joint angle body motion. Reference Trajectory Command is an important method to reduce vibration in flexible beam. This paper presents a very simple command control shaping which eliminates multiple mode residual vibration in a flexible beam combined fuzzy controller ...

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.4
• /
• pp.472-478
• /
• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The error back-propagation method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.4
• /
• pp.448-457
• /
• 1998

A neural network based control algorithm with fuzzy compensation is proposed for the automated adjustment in the production of electronic end-products. The process of adjustment is to tune the variable devices in order to examine the specified performances of the products ready prior to packing. Camcorder is considered as a target product. The required test and adjustment system is developed. The adjustment system consists of a NNC(neural network controller), a sub-NNC, and an auxiliary algorithm utilizing the fuzzy logic. The neural network is trained by means of errors between the outputs of the real system and the network, as well as on the errors between the changing rate of the outputs. Control algorithm is derived to speed up the learning dynamics and to avoid the local minima at higher energy level, and is able to converge to the global minimum at lower energy level. Many unexpected problems in the application of the real system are resolved by the auxiliary algorithms. As the adjustments of multiple items are related to each other, but the significant effect of performance by any specific item is not observed. The experimental result shows that the proposed method performs very effectively and are advantageous in simple architecture, extracting easily the training data without expertise, adapting to the unstable system that the input-output properties of each products are slightly different, with a wide application to other similar adjustment processes.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.2
• /
• pp.203-208
• /
• 2006

The management of image information settles as an important field with the advent of multimedia age and we are in need of the effective retrieval method to manage systematically image information. This paper has complemented the problem caused by the absence of space information that is a weak point of the existing color histogram method by assigning regions of features, and raised accuracy by adding texture and shape information. And existing methods using multiple features have problems that the retrieval process is embarrassed because each weight is set up manually. So we has solved these problems by assignment of weight applying fuzzy integral. As a result of experimenting with 1,000 color images, the proposed method showed better precision and recall than the existing method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.211-217
• /
• 1998

Many practical control problems for the complex, uncertain or large-scale plants, need to simultaneously achieve a number of objectives, which may conflict or compete with each other. If the conventional optimization methods are applied to solve these control problems, the solution process may be time-consuming and the resulting solution would ofter lose its original meaning of optimality. Nevertheless, the human operators usually performs satisfactory results based on their qualitative and heuristic knowledge. In this paper, we investigate the control strategies of the human operators, and propose a fuzzy model-based multi-objective satisfactory controller. We also apply it to the automatic train operation(ATO) system for the magnetically levitated vehicles(MAGLEV). One of the human operator's strategies is to predict the control result in order to find the meaningful solution. In this paper, Takagi-Sugeno fuzzy model is used to simulated the prediction procedure. Another str tegy is to evaluate the multiple objectives with respect to their own standards. To realize this strategy, we propose the concept of a satisfactory solution and a satisfactory control scheme. The MAGLEV train is a typical example of the uncertain, complex and large-scale plants. Moreover, the ATO system has to satisfy multiple objectives, such as seed pattern tracking, stop gap accuracy, safety and riding comfort. In this paper, the speed pattern tracking controller and the automatic stop controller of the ATO system is designed based on the proposed control scheme. The effectiveness of the ATO system based on the proposed scheme is shown by the experiments with a rotary test bed and a real MAGLEV train.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.6
• /
• pp.2393-2405
• /
• 2015

This paper considers a decentralized multiple faults detection and isolation (FDI) scheme for reconfigurable manipulators. Inspired by their modularization property, a global sliding mode (GSM) based stable adaptive fuzzy decentralized controller is investigated for the system in fault free, while for the system suffering from multiple faults (actuator fault and sensor fault), the decentralized sliding mode observer (DSMO) is employed to detect their occurrence. Hereafter, the time and location of faults can be determined by a fault isolation scheme via a bank of DSMOs. Finally, the effectiveness of the proposed schemes in controlling, detecting and isolating faults is illustrated by the simulations of two 3-DOF reconfigurable manipulators with different configurations successfully.