• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.3
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• pp.27-37
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• 1993

본 논문은 3S Look-up table을 이용한 직류서보 전동기 시스템의 퍼지 PID제어의 한 기법을 제안한다. 이러한 제어기법은 재래식 조정기법인 ITAE table을 이용하여 제어기의 변수를 1차 조정한 다음 퍼지제어 행렬을 사용하여 작은 이득갑의 변화를 자동 결정함으로서 전동기 속도제어 시스템의 정상상태오차와 과도응답 특성을 향상 시킬 수 있었다. 또한 이러한 시스템은 마이크로프로세서를 이용함으로서 쉽게 실현할 수 있었다.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1182-1184
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• 2001

This paper presents a neural network based self-tuning fuzzy PID control scheme for induction motor speed control. The PID controller is being widely used in industrial applications. When continuously used long time, the electric and mechanical parameters of induction motor change, degrading the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, and proposes a neural network based self-tuning fuzzy PID controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink is performed to verify the effectiveness of the proposed scheme.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.139-148
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• 1995

In this paper an electro-hydraulic servo system is designed by using a fuzzy control algorithm. In order to drive an optimal fuzzy control system, a simulation program for the control system has been developed. By this program the fuzzifier and defuzzifier, a fuzzy inference method, a fuzzy relational matrix, and a fuzzy inference method are investigated. As a result, Larsen inference method, 9*9 fuzzy relational matrix, and center of area defuzzifier are turned out the best as parameters. Finally this method is compared with the conventional PID algotithm, and showed that the fuzzy control performs better than PID algorithm. The fuzzy control performs very well adap- tation against uncertain disturbances.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.648-654
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• 2004

The Mass Flow Controller(MFC) has become crucial in semiconductor manufacturing equipments. It is an important element because the quality and the yield of a semiconductor process are decided by the accurate flow control of gas. Therefore, the demand for implementing the high speed and the highly accurate control of MFCs has been increasing. It is hard to find an article of the control algorithm applied to MFCs. But, it is known that commercially available MFCs adopt PID control algorithms. Particularly, when the system detects the flow by way of heat transfer, the MFC control problem includes the slow response and the nonlinearity. In this paper, MFC control algorithm with a superior performance to the conventional PID algorithm is discussed and the superiority is demonstrated through the experiment. A fuzzy controller was utilized in order to compensate the nonlinearity and the slow response, and the performance is compared with that of an MFC currently available in the market. The control system, in this paper, consists of a personal computer, the data acquisition board and the control algorithm carried out by LabWindows/CVI program on the PC. In addition, a method of estimating the actual flow from the sensor output with the slow response is presented. In conclusion, according to the result of the experiment, the proposed algorithm shows better accuracy and is faster than the conventional controller.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.444-451
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• 2007

In this paper, we propose an improved reduction model and a reduction model-based hybrid smith-predictor fuzzy controller. The transient and steady-state responsed of the reduction model was evaluated. In tuning the controller, the parameters of PID and the factors fuzzy controllers were obtained from the reduced model and by using genetic algorithms, respectively. Simulation examples demonstrated a better performance of the proposed controller than conventional ones.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.106-116
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• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.4
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• pp.543-554
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• 2000

The engineering equations, which have been used in many engineering companies, were employed for the dynamic modelling part in order to develop the naturally circulated boiler simulator. The fuzzy algorithm, which is similar to the algorithm of making decision by the human being, was developed for the boiler simulator controller and its simulated variables were compared with those of classical PID simulations to verify the stability and the effectiveness of fuzzy controller. The simulator is for the naturally circulated boiler and the main components are the furnace, the drum, the super heater, and the economizer. The combustion and thermal radiation dominant equations were used within the furnace and the mass conservation and the energy rate balance equations were employed for the drum part. The heat transfer rates were calculated using the logarithmic mean temperature differences both for the super heater and for the economizer. The simulations are very useful to understand the boiler operations and the engineering design of the main components. The main program was developed under the PC window condition by linking the fuzzy controller to the main boiler program using the Visual C++ language. The various operational conditions such as the abrupt changes of load, the changes of water supply pipes and the diameter of drum were simulated.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.5
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• pp.609-616
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• 2003

The purpose of this study is to design the adaptive speed control system of a marine diesel engine by combining the Model Matching Method and the Nominal Model Tracking Method. The authors proposed already a new method to determine efficiently the PID control Parameters by the Model Matching Method. typically taking a marine diesel engine as a non-oscillatory second-order system. But. actually it is very difficult to find out the exact model of a diesel engine. Therefore, when diesel engine model and actual diesel engine are unmatched as an another approach to promote the speed control characteristics of a marine diesel engine, this paper Proposes a Model Reference Adaptive Speed Control system of a diesel engine, in which PID control system for the model of a diesel engine is adopted as the nominal model and Fuzzy controller and derivative operator are adopted as the adaptive controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.701-708
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• 2017

This paper proposes an optimum design method using Teaching-Learning-based optimization for the fuzzy PID controller of Magnetic levitation rail-guided vehicle. Since an attraction-type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the conventional control methods. In the paper, a fuzzy PID controller with fixed parameters is applied and then the optimum parameters of fuzzy PID controller are selected by Teaching-Learning optimization. For the fitness function of Teaching-Learning optimization, the performance index of PID controller is used. To verify the performances of the proposed method, we use a Maglev model and compare the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.366-368
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• 1993

In this paper, we present an approach to automatically tune and adapt PID parameters by using Fuzzy Logic. PID controllers are well-known and found in many industries. Although the potentials of PID controllers, PID controllers are often poorly tuned and their capabilities are not fully used. We can think that the tuning of PID parameters is based on heuristics and some rule of thumbs. This is the reason we come to use Fuzzy Logic. We show that by imbedding heuristics and some rule of thumbs in PID controllers, reperesented by Fuzzy Logic. we can make PID controllers be robust to uncertainties such as load variations and adapt parameter changes.