• 제어로봇시스템학회논문지
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• 제18권6호
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• pp.526-531
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• 2012

This paper, we describe the vector control of surface mounted PMSM (Permanent Magnet Synchronous Motor) using the fuzzy controller which is suggested algorithm. In these days, when vehicle is operated or not, whether the road is covered or not, the sensitivity of the steering column is not stable. To make up for it, the PI gain of a steering column controller is adjusted by experience. It becomes the price because it need a lot of sensor. Also it is difficult to implement robust control because we need a lot of parameters for variable road conditions which are the off road, the on road, a low battery voltage, a high battery voltage, a vehicle speed. In this paper, we propose fuzzy controller using the suggested algorithm which suitable for steering system. We test the fuzzy controller with the various condition. We get the good performance of fuzzy controller even if it is nonlinear system. We check a robust the fuzzy controller using the suggested algorithm.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.199-202
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• 2001

An elevator system, which is essential equipment for vertical movement of an object, as a property of building, has been driven by various expenditures and purposes. Since developing electrical control technology, control system are highly developed. The elevator system has expanded widely, but a data accuracy acquisition technique and safety predict technique for securing system safety is still at a basic level. So, objective verification for elevator confidence condition requires an absolute accuracy measurement technique. Therefore, this study is executed in order to acquire a method of depending on sense of a manager with simple numeric measurement data, and to construct a logical, analytical foresight system for more efficient elevator management system. As an artificial intelligence for diagnosis, the fuzzy inference algorithm is used for foreseeing the system in this thesis, because the fuzzy algorithm is the most useful method for resolving subjective ideas and a vague judgment of humans. The fuzzy inference algorithm is developed for each sensor signal(i.e. vibration, velocity, current).

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1727-1730
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• 2003

A windshield wiper system plays a key part in assurance of driver's safety at rainfall. However, because quantity of rain and snow vary irregularly according to time and velocity of automotive, a driver changes speed and operation period of a wiper from time to time in order to secure enough visual field in the traditional windshield wiper system. Because a manual operation of windshield wiper distracts driver's sensitivity and causes inadvertent driving, this is becoming direct cause of traffic accident. Therefore, this paper presents the basic architecture of vision-based smart windshield wiper system and the rain sensing algorithm that regulate speed and operation period of windshield wiper automatically according to quantity of rain or snow. Also, this paper introduces the fuzzy wiper control algorithm based on human's expertise, and evaluates performance of suggested algorithm in simulator model. In especial, the vision sensor can measure wide area relatively than the optical rain sensor. hence, this grasp rainfall state more exactly in case disturbance occurs.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• 제어로봇시스템학회논문지
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• 제14권1호
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• pp.95-103
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• 2008

This paper deals with the problem of autolanding for aircraft under windshear environment for which the landing trajectory is given. It is well known that the landing maneuver in windshear turbulence is very dangerous and hard for the pilot to control because windshear is unpredictable in when and where it happens and its aerodynamic characteristics are complicated. In order to accomplish satisfactory autolanding maneuver in this environment, we propose a gain-scheduled controller. The proposed controller consists of three parts: PID controller, called baseline controller, which is designed to satisfy requirements of stability and performance without considering windshear, gain scheduler based on fuzzy logic, and safety decision logic, which decides if the current autolanding maneuver needs to be aborted or not. The controller is applied to a 6-DOF simulation model of the associated airplane in order to illustrate the effectiveness of the proposed control algorithm. It is noted that a cross wind in the lateral direction is included to the simulation model. From the simulation results it is observed that the proposed gain scheduled controller shows superior performance than the case of controller without gain scheduling even in severe downburst and tailwind region of windshear. In addition, touchdown along centerline of the runway is more precise for the proposed controller than for the controller without gain scheduling in the cross wind and the tailwind.

• 전기학회논문지
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• 제57권2호
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• pp.172-178
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• 2008

If the fault occurs on the underground power cable systems, the fault current on the sheath has an influence on all sections of cable because it's returned through earth at the directly grounded point and operation point of SVL(Sheath Voltage Limiter) on each insulated joint box. Therefore, the earth resistance and the operation of SVL have an effect on the zero-sequence current, and then the impedance between relaying point and fault point is increased. That causes the overreach of distance relay. For these reasons, the distance relay algorithm for protecting an underground power cable systems hasn't been developed till now. In this paper, new distance relay algorithm is developed for protecting a underground power cable system using fuzzy inference system which is the one of ACI(Advanced Computational Intelligence) techniques. This algorithm is verified by EMTP simulation of real power cable system, and proves to effectively advance the errors

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1140-1141
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• 2008

The paper proposes a novel control algorithm for tracking maximum power of PV generation system. The maximum power of PV array is determinated by a insolation and temperature. Prior considered the term in PV generation system is how maximum power point is accurately tracked. The paper proposes a Fuzzy-Neuro control algorithm so as to accurately track those maximum power points. The proposed control algorithm comprises the antecedence part of fuzzy rule and clustering method, multi-layer neural network in the consequent part. Fuzzy-Neuro has the advantages which are depicted both high performance and robustness in Fuzzy control and high adaptive control in Neural Network. Specially, it can show the outstanding control performance for parameter variations appling to non-linear character of PV array. In paper, the tracking speed and the accuracy prove the validity through comparing a proposed algorithm with a conventional one.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.201-203
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• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• 한국멀티미디어학회논문지
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• 제18권3호
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• pp.387-400
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• 2015

A fuzzy logic based mobile robot navigation system was developed to improve the driving ability without trapping inside obstacles in complex terrains, which is one of the most concerns in robot navigation in unknown terrains. The navigation system utilizes the data from ultrasonic sensors to recognize the distances from obstacles and the position information from a GPS sensor. The fuzzy navigation system has two groups of behavior rules, and the robot chooses one of them based on the information from sensors while navigating for the targets. In plain terrains the robot with the proposed algorithm uses one rule group consisting of behavior rules for avoiding obstacle, target steering, and following edge of obstacle. Once trap is detected the robot uses the other rule group consisting of behavior rules strengthened for following edge of obstacle. The output signals from navigation system control the speed of two wheels of the robot through the fuzzy logic data process. The test was conducted in the Matlab based mobile robot simulator developed in this study, and the results show that escaping ability from obstacle is improved.

• 한국항행학회논문지
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• 제13권6호
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• pp.813-819
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• 2009

무인항공기의 에일러론 및 플랩, 엘리베이터 등을 제어하기위한 작동기들은 구조적으로 Time variant한 비선형적인 특성을 가지고 있을 뿐 아니라, 비행 중에 풍향 및 풍량에 따라 모델링하기 힘든 외란이 발생할 경우가 많이 발생하기 때문에 우수한 제어성능을 보이는 제어기 설계에 많은 어려움이 있었다. 본 논문에서는 기존의 PID 제어기의 장점을 그대로 살리면서 실시간으로 변화하는 시스템에 adaptive하게 대응할 수 있고 Auto gain tuning을 이용하여 개발자의 시간과 노력을 현저히 줄일 수 있는 Fuzzy Auto gain tuning PID 제어 알고리즘을 비행체 Actuator 제어에 적용한 연구내용을 기술하였다.