• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.530-535
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• 2011

This paper deals with the precise temperature control of the heater used at a weaving thread or a drawn process. For precise temperature control, we suggest a design method that is auto-tuning IMC-PID controller using an optimization method uDEAS. For this method, we model the roll heater from the measurement data and we automatically tune the low pass filter value of IMC-PID controller that satisfies stability and conrol performance. Finally, we implement the designed controller using DSP kit.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.290-294
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• 2014

PID(Proportional, Integral, Derivative) controller is the most popular process controllers in nuclear power plants. The optimized parameter setting of the process controller contributes to the stable operation and the efficiency of the operating nuclear power plants. PID parameter setting is tuned when new process control systems are installed or current process control systems are changed. When the nuclear plant is shut down, a lot of PID tuning methods such as the Trial and Error method, Ultimate Oscillation method operation, Ziegler-Nichols method, frequency method are used to tune the PID values. But inadequate PID parameter setting can be the cause of the unstable process of the operating nuclear power plant. Therefore the results of PID parameter setting should be simulated, optimized and finally verified. This paper introduces the simulation method of PID tuning to optimize the PID parameter setting and confirms them of the actual PID controller in the operating nuclear power plants. The simulation method provides the accurate process modeling and optimized PID parameter setting of the multi-loop control process in particular.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.120-125
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• 2005

In this paper, We propose the fuzzy controller for the gain tuning of PID controller The proposed controller doesn't use the crisp output error and rule tables though with a fuzzy inference process in forward fuzzifier, New Fuzzy PID Controller assigns relations and ranges of two variables of PID gain parameters. These new gain parameters are calculated by the fuzzy inference with max-min ranges of Kp and Kd. The Ki parameter is computed automatically between Kp and Kd parameter Is calculated by Ziegler-Nickels tuning rules. Finally we experimented the propose controller by the hydraulic servo motor control system. We can obtained desired results through the good control characteristics.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2609-2614
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• 2011

A proportional integral derivative (PID) controller is designed and applied to a magnetic levitation conveyor system to control the levitation gap length of the electromagnet constantly. The PID gain parameters are optimized by response surface methods (RSM). The controller is verified with the state-space model of electromagnetic suspension by MATLAB/SIMULINK program. And, the controller and the state-space model are also verified experimentally. Simulation and experimental results shows the effectiveness of the PID gain tuning by RSM as compared with the classical PID tuning.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.268-273
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• 2005

The conventional PID controller has been widely used in many industrial control system because engineers can easily understand how to deal with three parameters of PID controller. The conventional tuning methods, however, have a tendency depend on experience and experiment. In this paper a real-coded genetic algorithm is used to search for the optimal parameters of PID controller for marine diesel engine. Simulation results compared with conventional PID controller tuning methods show the effectiveness and good performance of proposed scheme.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.12
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2470-2472
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• 2001

This paper describes the application of a fuzzy-tuning PID controller to a 3-DOF attitude control of a small model helicopter in hover for the compensation of coupling effects between each axis and system uncertainties due to the variation of engine RPM. A Low-level PID controller is designed by Ziegler-Nichols method and its gains are tuned by a high-level fuzzy system based on error states and its time derivatives. The experimental results show that the attitude control performance of fuzzy-tuning PID controller is improved comparing with that of a Ziegler-Nichols PID controller and fuzzy controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.05a
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• pp.90-94
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• 2004

Among various modern control theories, PID control has been well used for several decades. PID algorithms needs son tuning methods are used for selecting PID parameters. But in some cases various kinds of performance indices are used instead of well-known tuning rules, and so variable type of performance index must be tested so that controllers, output characteristics and disturbance rejection property meets some specifications. In this paper, linear conbinational type of performance using error signal, time, control input and robustness is used to the PID control of air conditioning system. By the 2 DOF PID parmeters minimizing perfromacne index, controllers, output characteristics and robustness properties are analyzed. Simulations are done with MATLAB m file and mdl files.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.830-836
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• 2006

In this paper described an auto tuning neuro-fuzzy PID controller based neural network. The PID type controller has been widely used in industrial application due to its simply control structure, easy of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. In this paper will design to take advantage of neural network fuzzy theory and pid controller auto toning technique. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. This controller simple structure and computational complexity are less, and also application is easy and performance is excellent in system that is strong and has nonlinearity to system dynamic behaviour change or disturbance. Finally, the proposed auto tuning neuro-fuzzy controller is applied to magnetic levitation. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.29-38
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• 1998

This paper proposes the neural network self-tuning PID controller for the load frequency control of 2- areas power system, namely, the prompt convergence of frequency and tie-line power flow deviation. The neural network applied to computer simulation consists of neurons of two inputs, ten hiddens and tliree outputs layer. Neurons of two inputs layer receive the error and its change rate of the system and cutputs layer consists of three neurons for the parameters of the PID controller. The simulation results shows that the proposed neural network self-tuning PID controller is superior to conventional control t~:chniques(Optimal, PID) in dynamic response and control performance.