• Proceedings of the KSRS Conference
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• v.2
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• pp.717-720
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• 2006

In this study, we predicted locations vulnerable to ground subsidence hazard using fuzzy logic and geographic information system (GIS). Test was carried out at an abandoned underground coal mine in Samcheok City, Korea. Estimation of relative ratings of eight major factors influencing subsidence and determination of effective fuzzy operators are presented. Eight major factors causing ground subsidence were extracted and constructed as a spatial database using the spatial analysis and the probability analysis functions. The eight factors include geology, slope, landuse, depth of mined tunnel, distance from mined tunnel, RMR, permeability, and depth of ground water. A frequency ratio model was applied to calculate relative rating of each factor, and the ratings were integrated using fuzzy membership function and five different fuzzy operators to produce a ground subsidence susceptibility map. The ground subsidence susceptibility map was verified by comparing it with the existing ground subsidences. The obtained susceptibility map well agreed with the actual ground subsidence areas. Especially, ${\gamma}-operator$ and algebraic product operator were the most effective among the tested fuzzy operators.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.599-609
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• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. First, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The control input for the system in the HFC combined PID controller with fuzzy controller is a convex combination of the FLC's output and PID's output by a fuzzy variable, namely, membership function of weighting coefficient. Second, an auto-tuning algorithms utilizing the simplified reasoning method and genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The proposed HFC is evaluated and discussed to show applicability and superiority with the and of three representative processes.

• Proceedings of the KIEE Conference
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• summer
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• pp.292-294
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• 2004

In this paper, we design the GA-fuzzy precompensator of a Power System Stabilizer for Thyristor Controlled Series Capacitor(TCSC-PSS) for enhancement of power system stability. Here a fuzzy precompensator is designed as a fuzzy logic-based precompensation approach for TCSC-PSS. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing TCSC-PSS. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership Auction and control rules. Simulation results show that the proposed control technique is superior to a conventional PSS in dynamic responses over the wide range of operating conditions and is convinced robustness and reliableness in view of structure.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Journal of the Korea Computer Industry Society
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• v.3 no.1
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• pp.95-104
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• 2002

This paper presents some studies on the Internet TCP/IP(Transmission Control Protocol-Internet Protocol) traffic over ATM(Asynchronous Transfer Mode) UBR(Unspecified Bit Rate) and ABR(Available Bit Rate) classes of service. Fuzzy logic prediction has been used to improve the efficiency and fairness of traffic throughput. For TCP/IP over UBR, a novel fuzzy logic based cell dropping scheme is presented. This is referred to as fuzzy logic selective cell drop (FSCD). A key feature of the scheme is its ability to accept or drop a new incoming packet dynamically based on the predicted future buffer condition in the switch. This is achieved by using fuzzy logic prediction for the production of a drop factor. Packet dropping decision is then based on this drop factor and a predefined threshold value. Simulation results show that the proposed scheme significantly improves TCP/IP efficiency and fairness. To study TCP/IP over ABR, we applied the fuzzy logic ABR service buffer management scheme from our previous work to both approximate and exact fair rate computation ER(Explicit cell Rate) switch algorithms. We then compared the performance of the fuzzy logic control with conventional schemes. Simulation results show that on zero TCP packet loss, the fuzzy logic control scheme achieves maximum efficiency and perfect fairness with a smaller buffer size. When mixed with VBR traffic, the fuzzy logic control scheme achieves higher efficiency with lower cell loss.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.148-154
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• 2012

Purpose: In order to improve road-friendliness of heavy vehicles, a fuzzy hybrid control strategy consisting of a hybrid control strategy and a fuzzy logic control module is proposed. The performance of the proposed strategy should be effectively evaluated using a hardware-in-the-loop (HIL) simulation model of a semi-active suspension system based on the fuzzy hybrid control strategy prior to real vehicle implementations. Methods: A hardware-in-the-loop (HIL) simulation system was synthesized by utilizing a self-developed electronic control unit (ECU), a PCI-1711 multi-functional data acquisition board as well as the previously developed quarter-car simulation model. Road-friendliness of a semi-active suspension system controlled by the proposed control strategy was simulated via the HIL system using Dynamic Load Coefficient (DLC) and Dynamic Load Stress Factor (DLSF) criteria. Results: Compared to a passive suspension, a semi-active suspension system based on the fuzzy hybrid control strategy reduced the DLC and DLSF values. Conclusions: The proposed control strategy of semi-active suspension systems can be employed to improve road-friendliness of road vehicles.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.2
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• pp.105-112
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• 2014

This paper proposes a novel $H{\infty}$ T-S Fuzzy controller with a weighted integral action for Interior Permanent Magnet Synchronous Motor(IPMSM) which have nonlinear dynamics. The $H{\infty}$ T-S Fuzzy controller is used for the robustness of nonlinear systems and the weighted integral action is used for the tracking problem and the improvement of control performance. A T-S Fuzzy controller is designed by combining the local controllers with the overall stability, and LMI(Linear Matrix Inequality)is used to determine the gains of linear controllers. The tracking problem of IPMSM is changed into regulator problem by introducing the integral action and the weighting factor gives flexibility to a $H{\infty}$ fuzzy controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.4
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• pp.388-398
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• 1998

In constructing fuzzy control systems. there are many parameters such as rule base. membership functions. inference m method. defuzzification. and I/O scaling factors. To control the system in properly using fuzzy logic. we have to consider t the correlation with those parameters. This paper deals with self-tuning of fuzzy control systems. The fuzzy controller h has parameters that are input and output scaling factors to effect control output. And we propose the looklongleftarrowup table b based self-tuning fuzy controller. We propose the PMTM(Plus-Minus Tuning Method) for self tuning method, self-tuning the initial look-up table to the appropriate table by adding and subtracting the values.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.195-203
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• 2016

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.