• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.342-345
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• 1995

This paper describes the design process and the experimental results of a fuzzy logic controller to control the tip position of a fixible-link manipulator, directly driven by a AC motor, with a large payload. The joint angle fuzzy logic controller is designed without a costly nonlinear system analysis of the flexible manipulator and the AC motor drive system. The state variables for the fuzzy logic controller are joint angle, joint velocity, link deflection, and link deflection velocity. The simulation and experimental results show that the joint position control is not satisfactory when the controller is designed under the assumption of no link flexibility and that stable joint position control and link vibration suppression can be cahieved with the fuzzy logic controller suggested in this paper.

• 제어로봇시스템학회논문지
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• 제18권2호
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• pp.133-140
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• 2012

In this paper, we suggest a lifelike pattern generator for a quadruped walking system with a head, a tail, four legs and a torso. The system looks like a giant dinosaur which stands over 7 meters tall with its legs over 2 meters long. We focus on its lifelike naturalness. Thus, generating logical patterns in harmony with head-body-tail patterns and quadrupedal locomotion patterns makes you feel that the quadruped walking system is alive. The basic patterns of four legs and a body are obtained from a 3D graphic animation, which is made and captured from various motions of similar species in existence since the giant dinosaurs are exterminated. The dinosaur-like mechanism also is designed from bone and joint structures of quadrupedal animals. The lifelike pattern generator based on fuzzy logic could generate lifelike motions according to the dinosaur-like mechanism and the basic patterns. A series of computer simulations and experimental implements show that the pattern generator makes the quadruped walking system lifelike.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.329-331
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• 2001

This paper representes a realization of a fuzzy logic speed controller for BLDC motor drives. Fuzzy sets are regulated by using parameters of BLDC motor. Simplified reasoning methods are used for defuzzification. Fuzzy logic speed controller is designed by using the high performance of DSPchip (TMS320F240). By experiment, it is confirmed that the speed of BLDC motor well follows an command speed in the load variables or low-speed area.

• 전기학회논문지
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• 제57권12호
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• pp.2285-2290
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• 2008

Even though efficiency of coal-fired power plant is proportional to operating temperature, increasement of operating temperature is limited by a technological level of each power plant component. It is an alternative plan to increase operating pressure up to ultra super critical point for efficiency enhancement. It is difficult to control process of power plant in ultra super critical point because that point has highly nonlinear characteristics. In this paper, new control logic, Unit Response Optimizer Controller(URO Controller) which is based on Fuzzy logic and Model Predictive Controller, is introduced for better performance. Then its performance is tested and analyzed with design guideline.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.342-344
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• 1994

This paper presents a design technique of SOFLC(Self -Organizing Fuzzy Logic Controller). It is composed of three parts: FLC(Fuzzy Logic Controller) part, RPO (Repeat Parameter Organizing) part, and RTPO (Real Time Parameter Organizing) part. The FLC part is controlled by initial parameters ($a_1$, $a_2$, $a_3$, $b_1$, $b_2$, $b_3$) the RPO part improves parameters by evaluating the performance of control responses controlled by FLC, and the RTPO organizes the parameters for real time in order to have the same value of the control response($y_k$) and the target response($y_k\;^*$).

• 한국정밀공학회지
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• 제21권3호
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• pp.30-37
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• 2004

This thesis develops a fuzzy-PID control algorithm for control the filament winding tension. It is developed by applying classical PID control technique to a fuzzy logic controller. It is composed of a fuzzy-PI controller and a fuzzy-D controller. The fuzzy-PI controller uses error and integrated error as inputs, and the fuzzy-D controller uses derivative of error as input. The fuzzy-PI controller uses Takagi-Sugeno fuzzy inference system, and the fuzzy-D controller uses Mamdani fuzzy inference system. The fuzzy rule base for the fuzzy-PI controller is designed using 19 rules, and the fuzzy rule base for the fuzzy-D controller is designed using 5 rules. A test-bed is set-up for verifying the effectiveness of the developing control algorithm in control the filament winding tension. It is composed of a mandrel, a carriage, a force sensor, a driving roller, nip rollers, a creel, and a real-time control system. Nip rollers apply a vertical force to a filament, and the driving roller drives it. The real-time control system is developed by using MATLAB/xPC Target. First, experiments for showing the inherent problems of an open-loop control scheme in a filament winding are performed. Then, experiments for showing the robustness of the developing fuzzy-PID control algorithm are performed under various working conditions occurring in a filament winding such as mandrel rotating speed change, carriage traversing, spool radius change, and reference input change.

• 한국자동차공학회논문집
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• 제21권2호
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• pp.72-80
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• 2013

This study is to develop and evaluate an AFS and an ARS controllers to enhance lateral stability of a vehicle. A sliding mode control (SMC) and a fuzzy logic control (FLC) methods are applied to calculate the desired additional steering angle of AFS equipped vehicle or desired rear steer angle of ARS equipped vehicle. To validate AFS and ARS systems, an eight degree of freedom, nonlinear vehicle model and an ABS controllers are also used. Several road conditions are used to test the performances. The results showed that the yaw rate of the AFS and the ARS vehicle followed the reference yaw rate very well within the adhesion limit. However, the AFS improves the lateral stability near the limit compared with the ARS. Because the SMC and the FLC show similar vehicle responses, performance discrimination is small. On split-${\mu}$ road, the AFS and the ARS vehicle had enhanced the lateral stability.

• 전기학회논문지P
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• 제67권3호
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• 한국자동차공학회논문집
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• 제16권4호
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• pp.159-164
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• 2008

In diesel engine technology the drive to reduce emissions and fuel consumption with improved performance targets has led to many advances. In particular, Exhaust Gas Recirculation (EGR) and Variable Geometry Turbocharger (VGT) have played a key role in achieving these aims by permitting flexible control of the engine inlet gas charge. The full potential of these devices are difficult to achieve due to limitations in the classical control methods. However, fuzzy logic is particularly appealing due to its simple heuristic nature. The controller used in this work was designed using the Matlab Fuzzy Logic Toolbox. The overall object is to access the potential for emissions and fuel consumption reductions during transient events whilst maintaining and even improving driveability. Classical control methods (PID), as used on production engines, are examined and contrasted with an coordinated control that utilizes fuzzy logic.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 춘계학술대회 논문집 전력기술부문
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• pp.210-213
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• 2001

This study presents an effective variable forgetting factor method based on fuzzy logic to suppress frequency droop in extended integral load frequency control. The performance of the extended integral control is greatly dependent on the decaying factor. For an optimal or near optimal performance, it is necessary that the decaying factor as well as the feedback gains should be changed very quickly in response to changes in the system dynamics. However, because of its time-varing characteristic, the optimal decaying factor is difficult to be selected analytically. By adopting fuzzy set theory, the decaying factor can be determined quickly to respond to the variation of the feedback signals. This study builds a fuzzy rule base with use of the change of frequency and its rate as inputs. The computer simulation has been conducted for the single machine system. The simulation results show that the proposed fuzzy 1o81c based controller yields more improved control performance than the conventional PI controller.