• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.709-711
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• 1999

In this paper, Fuzzy Logic Controller was Designed for the Degree control of Arago's disk system. Arago's disk system is an application of Arago's disk phenomenon which is the operating principle of induction motor. Since the Arago's disk system varies to stable region. maginally stable region, unstable region according to the degree of bar respectively, it is a sutable system which can be evaluate an efficiency of the system. While an existing controller which was designed using linearized system modeling could control the system on only one operating point, fuzzy logic controller has the advantage in showing good response for multi-operating points because it does not need system modeling.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1223-1232
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• 2004

We present a new technique to the design and real-time implementation of fuzzy control system basedon digital signal processors in order to improve the precision and robustness for system of industrial robot in this paper. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C80 is used in implementing real time fuzzy control to provide an enhanced motion control for robot manipulators. In this paper, a Self-Organizing Fuzzy Controller for the industrial robot manipulator with a actuator located at the base is studied. 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 problems is the determination of linguistic control rules from the human operators. To overcome this difficult Self-Organizing Fuzzy Controller is proposed for a hierarchical control structure consisting of basic and high levels that modify control rules. The proposed Self-Organizing Fuzzy Controller scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the SOFC is illustrated by simulation and experimental results for a Dual-Arm robot with eight joints.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2001년도 The Seoul International Simulation Conference
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• pp.103-109
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• 2001

Soft sensor is an effective method to deal with the estimation of variables, which are difficult to measure because of the reasons of economy or technology. Fuzzy logic system can be used to develop the soft sensor model by infinite rules, but the fuzzy dividing of variable sets is a key problem to achieve an accurate fuzzy logic model, In this paper, we proposed a new method to develop soft sensor model based on fuzzy neural network. First, using a novel method to divide the variable fuzzy sets by the process input and output data. Second, developing the fuzzy logic model based on that fuzzy set dividing. After that, expressing the fuzzy system with a fuzzy neural network and getting the initial soft sensor model based FNN. Last, adjusting the relative parameters of soft sensor model by the BP learning method. The effectiveness of the method proposed and the preferable generalization ability of soft sensor model built are demonstrated by the simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.128.4-128
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• 2001

Based on fuzzy logic algorithm this paper constructed fuzzy logic controller for automated vehicles. For passenger´s convenience especially comfortability controller need to reduce the frequency of input variable´s changing. So we established membership functions for comfortability as well as speed following. It made possible to control comfortability directly. To demonstration the efficiency of fuzzy logic controller, we carried out simulation with a automobile´s transfer function. First, we designed the PID controller by using Ziegler-Nichols tunning method. Second, we calculated time response for each controller, then we compared the speed patterns of fuzzy controlled system and PID controlled system. Also we compared the difference of input variable ...

• Journal of Electrical Engineering and Technology
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• 제5권3호
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• pp.415-422
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• 2010

The distributed flexible alternative current transmission system (D-FACTS) is a recently developed FACTS technology. Distributed Static Series Compensator (DSSC) is one example of DFACTS devices. DSSC functions in the same way as a Static Synchronous Series Compensator (SSSC), but is smaller in size, lower in price, and possesses more capabilities. Likewise, DSSC lies in transmission lines in a distributed manner. In this work, we designed a fuzzy logic controller to use the DSSC for enhancing transient stability in a two-machine, two-area power system. The parameters of the fuzzy logic controller are varied widely by a suitable choice of membership function and parameters in the rule base. Simulation results demonstrate the effectiveness of the fuzzy controller for transient stability enhancement by DSSC.

• Journal of Power Electronics
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• 제16권3호
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• 한국컴퓨터정보학회논문지
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• 제4권4호
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• pp.1-8
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• 1999

인간의 사고 방법과 그 원리를 연구하는 논리에서 이진 논리는 True 혹은 False 중 하나만을 진리값으로 갖는 명제를 다룬다. 인간이 다루는 대부분이 기존의 이치논리는 다루기 힘든 애매성(Fuzziness)을 포함하고 있음에도 불구하고 이를 표현하는 지식은 부정확하며 신뢰성은 떨어지게 된다. 이러한 문제점을 해결하기 위하여 본 논문에서는 다치 논리를 사용하였으며, 다치 논리는 2치 논리에 비하여 동일정보량을 처리하는데 고속 처리가 가능한 장점을 가지고 있다. 그래서 2치 부호화를 기본으로한 정보시스템에 비해 다치 부호화 알고리즘을 사용하여 구성하는 기호다치논리를 사용하여 Fuzzy Inference에서 사용되는 Fuzzy Rule을 효과적으로 Minimization하여 추론이 가능하도록 하는 것이다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권1호
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• pp.12-18
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• 2011

This paper deals with a new adaptive fuzzy sliding mode controller and its application to an inverted pendulum. We propose a new method of adaptive fuzzy sliding mode control scheme that the fuzzy logic system is used to approximate the unknown system functions in designing the SMC of uncertain nonlinear systems. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1500-1504
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• 2005

This paper proposes the design methodology of genetic algorithm (GA)-based fuzzy Kalman filter for tracking the maneuvering target. The performance of the standard Kalman Filter (SKF) has been degraded because mismatches between the modeled target dynamics and the actual target dynamics. To solve this problem, we use the method to estimate the increment of acceleration by a fuzzy system using the relation between maneuver filter residual and non-maneuvering one. To optimize the fuzzy system, a genetic algorithm (GA) is utilized and this is then tuned by the fuzzy logic correction. Finally, the tracking performance of the proposed method has been compared with those of the input estimation (IE) technique and the intelligent input estimation (IIE) through computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.