• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.687-692
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• 1991

To reduce the complexity in constructing a fuzzy logic controller of multivariable systems, three major methods are presented. One is the method of constructing single-input-single-output fuzzy logic controllers after decoupling the target system. Another is the method of using fuzzy relation matrices which indicate the relation between each input and output. The other is the method of using the hierarchically classified inputs which dominantly influence one output than other inputs. Using the last two methods, simulation results of fuzzy logic controller implemented on 160MW boiler-turbine plant model are also shown.

• Proceedings of the KIPE Conference
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• 2011.11a
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• pp.285-286
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• 2011

Maximum Power Point Tracker (MPPT) is the big issue in generating power based on Wind Energy Conversion System. In case of unknown turbine characteristic, it is useful to implement MPPT based on fuzzy logic control. This kind of control is able to find the value of duty cycle to meet maximum power point at particular wind speed. There are many methods to develop MPPT based fuzzy logic controls. In this paper, two of the methods are compared both at low and high fluctuating wind speed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.50-54
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• 1997

In general, the design of fuzzy logic controller has difficulties in the acquisition of expert's knowledge. So, some methods that can optimize the parameters for fuzzy logic controller automatically without expert knowledge was provided. Recently, tuning method for fuzzy logic controller using genetic algorithm(GA) were proposed in many papers. However, those are tuning methods for a part or some part of fuzzy logic controller. In this paper, we proposes auto tuning method for the whole part of tuzzy logic controller, such as parameters of membership functions for antecedence and consequence parts, rule base, scaling factor and the number of rule. Finally, second order dead time plant is provided to show the advantages of the proposed method.

• Journal of KIISE:Information Networking
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• v.31 no.3
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• pp.289-297
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• 2004

State-of-the-art wireless communication networks allow dynamic relocation of mobile terminals. A location management mechanism is required to keep track of a mobile terminal for delivering incoming calls. In this paper, we propose a fuzzy logic-based selective paging method to reduce paging cost. In the proposed fuzzy logic-based location management method, the location update uses the area-based method that uses direction-based together with movement-based methods, and the location search uses the fuzzy logic-based selective paging method based on the mobility information of mobile terminals. A partial candidate paging area is selected by fuzzy control rules, then the fuzzy logic-based selective paging method pages only the cells within the partial candidate paging area. The performance of proposed fuzzy logic-based location management method is to be evaluated by both an analytical model and a simulation, and is compared with those of LA and BVP methods. From these evaluation results, we know that the proposed fuzzy logic-based location management method provide better performance than other location management methods.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.3
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• pp.468-477
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• 1994

Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unkonwn deadzones. In particular, we show that a conventional fuzzy logic controller applied to a system with a deadzone suffers from poor transient performance and a large steady-syate error. In this paper, we propose a novel two-layered fuzzy logic controller for controlling systems with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator followed by a conventional fuzzy logic controller. Our proposed controller exhibits superior transient and steady-state performance compared to conventional fuzzy controllers. In addition, the controller is robust to variations in deadzone nonlinearities. We illustrate the effectiveness of our scheme using computer simulation examples.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2258-2263
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• 2003

This paper presents the design and development of an optimal fuzzy logic controller (FLC) for a laser tracking system. An optimal intelligent fuzzy logic controller was founded on integral criterion of the fuzzy models and three-dimensional fuzzy control. Research had been also concentrated on the methods for multivariable fuzzy models for the purposes of real-time process. Simulation results have shown remarkable tracking performance of this fuzzy PID controller.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_1
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• pp.249-256
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• 2024

Traditional methods for monitoring targets rely heavily on probabilistic data association (PDA) or Kalman filtering. However, achieving optimal performance in a densely congested tracking environment proves challenging due to factors such as the complexities of measurement, mathematical simplification, and combined target detection for the tracking association problem. This article analyzes a target tracking problem through the lens of fuzzy logic theory, identifies the fuzzy rules that a fuzzy tracker employs, and designs the tracker utilizing fuzzy rules and Kalman filtering.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.5
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• pp.196-206
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• 2000

These days, the fuzzy logic or the fuzzy set theory has received attention from a number of researchers in the area of industrial application. Moreover, the fuzzy logic control has been successfully applied to a large numbers of control problems where the conventional control methods had failed. Using this control theory we designed a climate controller for an automotive climate control system whose mathematical model is difficult. This paper describes an automotive climate control where the fuzzy control has been used to stabilize parameter uncertainties and disturbance effects. To show the validity and effectiveness of the proposed control method, the fuzzy logic controller was implemented with a philips 80C552 microcomputer chip and tested in an actual vehicle. From the experimental results, it could be conduced that the proposed controller is superior to conventional controllers in both control performance and thermal comfort. The climate control system in cars is difficult to model mathematically so we tested a fuzzy logic control system which promised better results.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.503-510
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• 2010

This paper proposes the use of fuzzy applications to a 4-machine and 10-bus system to check stability in open conditions. Fuzzy controllers and the excitation of a synchronous generator are added. Power system stabilizers (PSSs) are added to the excitation system to enhance damping during low frequency oscillations. A fuzzy logic power system stabilizer (PSS) for stability enhancement of a multi-machine power system is also presented. To attain stability enhancement, speed deviation ($\Delta\omega$) and acceleration ($\Delta\varpi$) of the Kota Thermal synchronous generator rotor are taken as inputs to the fuzzy logic controller. These variables have significant effects on the damping of generator shaft mechanical oscillations. The stabilizing signals are computed using fuzzy membership functions that are dependent on these variables. The performance of the fuzzy logic PSS is compared with the open power system, after which the simulations are tested under different operating conditions and changes in reference voltage. The simulation results are quite encouraging and satisfactory. Similarly, the system is tested for the different defuzzification methods, and based on the results, the centroid method elicits the best possible system response.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.