• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.21-24
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• 2001

This paper presents a loop shaping procedure for tuning optimal and robust PID controller by selecting the appropriate weighting factors Q and R in order to satisfy such the performance requirements.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.847-852
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• 1993

In this paper, we present a recursive algorithm for the auto-tuning of PID controllers by optimizing a GPC criterion. Also, we develop an intelligent PID controller by combination of a recursive algorithm together with a supervisor, that allows to adjust the main controller parameters (prediction horizon, control weighting, sample time etc.) using some simple rules which is mainly built up through relay tuning experiments. The intelligent PID controller has been implemented successfully on an IBM PC/AT and some simulation results are presented.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2102-2104
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• 2001

This paper present the strategy of self-tuning nonlinear PID control using neural network. The nonlinear PID controller consists of a conventional PID controller and a neural network compensator. The neural network is trained by back-propagation algorithm. In this paper we propose modified back-propagation algorithm to improve learning speed. The results of simulation show the usefulness of the proposed scheme.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.1
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• pp.40-46
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• 1999

A self-tuning FPID(Fuzzy Proportional Intergral Derivative) controller fo load frequency control of 2-area power systemis proposed in this paper. The paramters of the proposed self-tuning FPID controller are self-tuned by the proposed fuzzy inference technique. Therefore in this paper the fuzzy inference technique of PID gains using PSGM(Product Sum Gravity Method) is presented and is applied to the load frequency control of 2-area power system. The computer simulation results show that the proposed controller give better more control characteristics than convention-al PID, FLC under load changes.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1092-1095
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• 1996

In this paper The tuning of PID controller for multi input-output is studied by using fuzzy inference. State of coupling is estimated by fuzzy inference, its results is used for tuning of PID controller to get optimum P,I,D parameter with regard to state of coupling. This method is simulated to Turbo-generating system with $2{\times}2$ multi input-output and made with electronic circuit, its response is very satisfactory.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.2
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• pp.54-58
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• 2002

PID Controller is widely used as automatic equipment for industry. However, when a system has various characters, parameter decision and tuning for accurate control is a hard task. In this paper, expert auto-tuning PID controller using PLC is presented as away of solving this problem. Expert auto tuning algorithm is based on Ziegler-Nichols step response and expert knowledge. The test of control performance is carried out in practical speed control of Induction Motor in variable load, the experimental results suggest its superior performance.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.974-981
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• 2009

In this paper, a method of auto-tuning of PID (Proportional-Integral-Derivative) and PIDA (Proportional-Integral-Derivative-Acceleration) controllers is proposed that can be applied to a time-delayed second order model. The proposed identification method is based on step responses, but it can be easily automated rising digital controller unlike the existing graphical identification methods. We provide a ways to yield parameter identifications which is independent to initial values of the plants. The tuning rule is based on the pole-placement strategy and is formulated so that it can be implemented using a digital controller with ease.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.7
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• pp.1003-1011
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• 2009

This study aims at precise control of oil outlet temperature in the oil cooler system of machine tools for enhancement of working speed and processing accuracy. PID control logic is adopted to obtain desired oil outlet temperature of the oil cooler system with hot-gas bypass method. We showed that the gains of PID controller could be easily determined by using gain tuning methods to get the gain of PID controller without any mathematical model. We also investigated various gain tuning methods to design the gains of PID and compared each control performance for selecting the optimal tuning method on the hot gas bypass method through experiments. Moreover, we confirmed excellent control performance with proposed PI controller gain even though disturbances were abruptly added to the experimental system.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2651-2654
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• 2000

In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a self tuning fuzzy PID controller whose input output scaling factors were tuned automatically. At first stage, the tuning parameters of fuzzy controller were determined by Ziegler-Nichols tuning method and then they were adjusted as the delay time and process environment were changed. Proposed controller was simple in its structrue and computational burden was small so that on line adaptation was easy to apply to. The result of computer simulation and practical experiment showed the proposed controller's excellent performance

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.436-438
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• 1993

A rule based fuzzy expert system in self-tune PID controllers is presented in this paper. The rule base. the core of the expert system, is extracted from the Wills' tuning map and the author's knowledge about the implicit relations between PID gains and controlled output response. The overall control system consists of the relay feedback scheme and the expert system, where the one is responsible for initial tuning and the other for subsequent tuning. The PID control system with the proposed fuzzy expert system, shows better convergence rate and control performances than those of a Litt in spite of the fact that the two rule bases are extracted from the same maps provided by Wills.