• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.80-83
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• 2003

A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.14-19
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• 1997

In this paper, a new fuzzy sliding mode control algorithm is presented for trajectory control of robot manipulators. A fuzzy logic is applied to a sliding mode control algorithm to have the sliding mode gain adjusted continuously through fuzzy logic rules. With this scheme, te stability and the robustness of the proposed fuzzy logic control algorithm are proved and ensured by the sliding mode control law. The fuzzy logic controller requires only a few tuning parameters to adjust. Computer simulation results are given to show that the proposed algorithm can handle uncertain systems with large parameter uncertainties and external disturbances.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.104-111
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• 2000

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2270-2273
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• 2003

Fuzzy logic control has been widely implemented in air conditioning and ventilation systems which has uncertainty or high robust system. Since the dynamic behaviors of the systems contain complexity and uncertainty in its parameters , several fuzzy logic controllers had been implemented to control room temperature in the field of air conditioning system. In this paper, the fuzzy logic control has been developed to control room temperature and humidity in the precision air conditioning systems. The nonlinear mathematical model was formulated using energy and continuity equations. MATLAB was used to simulate the fuzzy logic control of the multi-variable air conditioning systems. The simulation results show that fuzzy logic controller can reduce the steady-state errors of the room temperature and relative humidity in multivariable air conditioning systems. The offset are less than 0.5 degree Celsius and 3 percent in relative humidity respectively under random step disturbance in heating load and moisture load respectively

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.49-53
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• 2008

A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.5
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• pp.23-29
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• 1994

The design procedure for fuzzy logic controller depends on the expert's knowledge or trial and error. Moreover, it is very difficult to guarantee the stability and robustness of the system due to the linguistic expression of fuzzy control. However, fuzzy logic control has succeeded in many control problems that the conventional control theory has difficulties to deal with. As a result, this control theory is applied to the engine control system which a mathematical model is difficult. In this study, the fuzzy logic is applied to obtain the gain of PI control at idle speed control system, and a simple engine model is developed in order to perform simulation. Experimental results show that the response to reach the target engine speed at idle speed control system is improved by adopting the gain obtained with fuzzy logic.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.457-459
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• 2003

This paper considers the application of Fuzzy Logic to Smart Decision process of Smart Sensor system that interprets and response to the change of environmental parameters. The considered system consists of three sensors: temperature sensor, humidity sensor and pressure sensor. The smartness of system is constituted by the applying of Fuzzy Logic. The paper discusses the technical details of the application of Fuzzy Logic for making the system to be smarter.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.122-126
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• 1990

This paper discusses the possibility of applying fuzzy logic controller in a microprocessor-based brushless DC servo motor controller, which requires faster and more accurate response compared with other industrial processes. Limitations of fuzzy logic controller are also described.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.640-643
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• 1997

This paper describes speed controller of a induction motor for electric vehicles using PLL and Fuzzy logic. The proposed system is combined precise speed control of PLL and robust, fast speed control of Fuzzy logic. The motor speed is adaptively incremented or decremented toward the PLL locking range by the Fuzzy logic using information of sampled speed errors and then is maintained accurately by PLL. The results of experiment show excellence of proposed system and that the proposed system is appropriates to control the speed of induction motor for electric vehicles.