• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.826-829
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• 1993

Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. 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 pre-compensator followed by a conventional fuzzy logic controller. Our proposed controller exhibits superior transient and steady-state performance compared to conventional fuzzy controllers. We illustrate the effectiveness of our scheme using computer simulation examples.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.238-242
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• 1991

This paper presents an approach to self-tuning PI control of dynamic plants, based on fuzzy logic application. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a fuzzy logic controller, one of the most difficult problem is the selection of linguistic control rules and parameters. To overcome this difficulty, self-tuning fuzzy PI controller (STFPIC) with a hierarchical structure in which the fuzzy PI controller is assigned as the lower level and the rule modification and parameter adjustment as the higher level. The rules and parameters are generated by the adjustment of membership function through performance index(PE). In this paper, the algorithm for of the controller performance is estimated by means of computer simulation.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.481-486
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• 2018

DC motors are classified as DC motors with brush structure and BLDC motors without brush structure. Representing the speed control of the BLDC motor is the PI control. The speed control using the PI controller has a disadvantage that the response characteristic to reach the steady state is slow. Therefore in this paper, a voltage controlled speed controller using a Fuzzy Logic Controller (FLC), which has a short steady response time and usefulness of nonlinear control. The validity and usefulness of the proposed fuzzy speed controller are verified by simulation through Simulink of MATLAB program. Experiments were performed on the PI controller and the proposed fuzzy speed controller in three cases with reference speeds of 500rpm, 800rpm, and 1500rpm. Experimental results show that the proposed fuzzy controller has more 30% improved steady state speed response than PI controller.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05a
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• pp.121-126
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• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLC[8] for. three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[8].

• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.212-220
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• 2007

This paper proposes a method of avoiding obstacles and tracking a moving object continuously and simultaneously by using new concepts of virtual tow point and fuzzy danger factor for differential wheeled mobile robots. Since differential wheeled mobile robot has smaller degree of freedom to control and are non-holonomic systems, there exist multiple solutions (trajectories) to control and reach a target position. The paper proposes 'fuzzy danger factor' for obstacles avoidance, 'virtual tow point' to solve non-holonomic object tracking control problem for unique solution and three kinds of fuzzy logic controller. The fuzzy logic controller is policy decision controller with fuzzy danger factor to decide which controller's result is more valuable when the mobile robot is tracking a moving object with obstacles to be avoided.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.141.1-141
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• 2001

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) for a high maneuverable aircraft system, where the gains of FLC are on-line adapted according to the flight condition. Specially, the systematic procedure via root locus technique is carried out for the sellection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields better control performance than the normal (without gain scheduling) fuzzy controller.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.4
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• pp.393-399
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• 2003

In this paper, a self-organizing fuzzy logic controller with hybrid structure is proposed. The structure of the proposed method is composed of a basic fuzzy logic controller and the FARMA SOC(Fuzzy Autoregressive Moving Average Self-organizing Controller). The self-organizing cntroller with hybrid structure has advantage over the FARMA controller as follows. The proposed controller improves poor performance due to the lack of I/O data to calculate predictive output. I executed some computer simulations on the regulation problem of an inverted pendulum system and compared the results of the proposed method with those of the FARMA SOC method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.910-914
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• 2005

In this paper an optimal method based on fuzzy logic for controlling parallel hybrid electric vehicles is presented. In parallel hybrid electric vehicles the required torque for deriving and operating the on-board accessories is generated by a combination of internal-combustion engine and an electric motor. The powersharing between the internal combustion engine and the electric motor is the key point for efficient driving. This is a highly nonlinear and time varying plant and its control strategy will be implemented with the use of fuzzy logic controller. The fuzzy logic controller will be designed based on the state of charge of batteries and the desired torque for driving. The output of controller controls the throttle of the combustion engine. The main contribution of this paper is the development of an optimal control based on fuzzy logic, which maximizes the output torque of the vehicle while minimizing fuel consumed by the combustion engine.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.777-780
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• 1999

In this paper, a fuzzy logic controller is designed for speed control of a hydraulic inverter elevator. Mathematical modeling of an elevator actuated with hydraulic system is presented and the friction characteristics of a cylinder is examined, which may cause the abrupt increase of the acceleration in the zero-crossing speed region. Simulation results show that the proposed fuzzy logic speed controller yields a better control performance than conventional PID controller.