• 제어로봇시스템학회논문지
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• 제14권1호
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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.

• 제어로봇시스템학회논문지
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• 제3권6호
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• pp.588-593
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• 1997

This paper shows a design and implementation of throttle valve controller for engine dynamometer system using fuzzy logic. Recently, we demanded the excellent measuring equipment so as to improve engine performance. The throttle valve control for engine dynamometer system is a very particular part in the engine control. Since the structure of engine dynamometer system is very complicated and has nonlinear elements which are influenced by disturbance of vibration, heating, cooling, and energy loss so on. In this paper, fuzzy logic control application have been successful in throttle valve control problem for engine dynamometer system in which the conventional control had difficulties dealing with the system. In this study, we propose a method that the control strategy uses Fuzzy Look-up table and normalization and obtained the satisfying result from realized throttle valve controller for engine dynamometer system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.695-698
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• 1995

The shape and width of fuzzy membership function has an effect on performance of fuzzy controller. In this paper, fuzzy controller is proposed to improve the control performance of fuzzy controller. It has two fuzzy inference engine. The one is typical fuzzy inference engine, the other is proposed to infer optimal width of membership function in fuzzy controller from plant constant (K,T,L). To show the effectiveness of this fuzzy controller with double fuzzy inference engine, it is applied to plant (dead time + 1st order delay) with various plant constant.

• 전자공학회논문지B
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• 제29B권7호
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• pp.30-41
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• 1992

As a general model of rule-based systems, we propose a model for a fuzzy production system having chaining rules and an inference engine associated with the model. The concept of so-called 'fuzzy petri nets' is used to model the fuzzy production system and the inference engine is designed to be capable of handling inexact knowledge. The fuzzy logic is adopted to represent vagueness in the rules and the certainty factor is used to express uncertainty of each rules given by a human expert. Parallel, inference schemes are devised by transforming Fuzzy Petri nets to matrix formula. Futher, the inference engine mechanism under the Mamdani's implication method can be desceribed by a simple algebraic formula, which makes real time inference possible.

• 한국자동차공학회논문집
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• 제2권5호
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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.

• 한국항행학회논문지
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• 제20권3호
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• pp.190-195
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• 2016

본 논문에서는 무인항공기용 소형 터보제트엔진에 대해 압축기 서지현상 및 화염소실을 방지하면서 과도응답 특성을 개선하는 제어기를 설계하였다. 터보제트 엔진의 가 감속 시 서지현상과 flame-out 현상을 방지하기 위해 연료 유량 제어 입력을 Fuzzy-PID 제어기로 생성하고 신속하고 안전하게 원하는 속도로 수렴할 수 있도록 제어기 설계한다. LabVIEW을 이용한 시뮬레이션을 통해 PID와의 응답특성 비교 분석 및 신속하고 안전하게 원하는 속도로 수렴하는 제어 성능을 확인하였다.

• Advances in Computational Design
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• 제4권4호
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• pp.367-379
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• 2019

Multiple Inputs and Multiple Outputs (MIMO) Fuzzy logic model is developed to predict the engine performance and emission characteristics of pongamia pinnata biodiesel with hydrogen injection. Engine performance and emission characteristics such as brake thermal efficiency (BTE), brake specific energy consumption (BSEC), hydrocarbon (HC), carbon monoxide (CO), carbon dioxide ($CO_2$) and nitrous oxides ($NO_X$) were considered. Experimental investigations were carried out by using four stroke single cylinder constant speed compression ignition engine with the rated power of 5.2 kW at variable load conditions. The performance and emission characteristics are measured using an Exhaust gas analyzer, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends (Diesel, B10, B20 and B30) and engine load conditions. Fuzzy logic model uses triangular and trapezoidal membership function because of its higher predictive accuracy to predict the engine performance and emission characteristics. Computational results clearly demonstrate that, the proposed fuzzy model has produced fewer deviations and has exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.99136 to 1 with experimental values. The developed fuzzy logic model has produced good correlation between the fuzzy predicted and experimental values. So it is found to be useful for predicting the engine performance and emission characteristics with limited number of available data.

• 한국항공운항학회지
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• 제17권4호
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• pp.1-7
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• 2009

The surge control system in unmanned turbojet engine must be capable of accounting uncertainties from engine transient conditions, random fluctuations of key parameters such as air pressure and fuel flow and engine modeling errors. In this paper, taking into consideration of its effectiveness as well as system stability, a fuzzy PI controller is proposed. The role of the fuzzy PI controller is to stabilize the unmanned aircraft upon occurring unexpected engine surge. The proposed control scheme is proved by computer simulation using a linear engine model. The simulation results on the state space model of a small turbojet engine illustrate the proposed control system achieves the desired performance.

• Journal of Advanced Marine Engineering and Technology
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• 제24권4호
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• pp.435-440
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• 2000

The speed control system of diesel engine is considerably nonlinear. Therefore, a countermeasure such as gain scheduling used to be incorporated to compensate this nonlinearity. On the other hand, it is said that fuzzy control is very robust against nonlinearity. But it is difficult to get a satisfactory response with only fuzzy control in real system. In this paper authors design a fuzzy-PI controller for the speed control of Medium diesel engine and carry out experiments with dedicate system implemented by Intel 80916KC to real diesel engine, Deawoo MAN 6Cyl., 1800rpm driving 3$\psi$220V, 150KW generator. We confirm the effectiveness of proposed control system.

• 한국정밀공학회지
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• 제20권11호
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• pp.91-99
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• 2003

When the driver suddenly depresses the brake pedal under critical conditions, the desired trajectory of the vehicle can be changed. In this study, the vehicle dynamics and fuzzy logic controller are used to control the vehicle trajectory. The dynamic vehicle model consists of the engine, the rotational wheel, chassis, tires and brakes. The engine model is derived from the engine experimental data. The engine torque makes the wheel rotate and generates the angular velocity and acceleration of the wheel. The dynamic equation of the vehicle model is derived from the top-view vehicle model using Newton's second law. The Pacejka tire model formulated from the experimental data is used. The fuzzy logic controller is developed to compensate for the trajectory error of the vehicle. This fuzzy logic controller individually acts on the front right, front left, rear right and rear left brakes and regulates each brake torque. The fuzzy logic controlling each brake works to compensate for the trajectory error on the split - $\mu$ road conditions follows the desired trajectory.