• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.102-107
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• 2008

We propose an autotuning algorithm for PI controller with unknown plant. The proposed algorithm uses a saturation function and time delay element as a test signal. Since the integral element of PI controller reduces a phase margin in the closed loop system, the closed loop system could be resulted in unstable with PI controller. To avoid unstable in the closed loop system with PI controller, the proposed algorithm identifies one point information in the 3rd quadrant of Nyquist plot with a time delay element. The proposed method improves an accuracy of one point identified information with one saturation function. We demonstrate a performance of the proposed method via a simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.257-262
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• 1994

This paper describes an autotuning fuzzy PID controller for a position control of DC serve motor. Because ZNM(Ziegler-Nichols Method) with relay feedback has the difficulty in re-tuning the PID parameters and adaptive method has complex algorithm, a new method to overcome those problems is required. The proposed scheme determines the initial PID gains by using ZNM with relay feedback, and then re-tunes the optimal PID parameters by using fuzzy expert system whenever control conditions are changed. To show the validity of the proposed method, a position control of DC servo motor is illustrated by computer simulation and is experimented by a designed controller.

• Journal of IKEEE
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• v.2 no.2 s.3
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• pp.263-269
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• 1998

We use the slope bounded saturation nonlinear feedback element instead of relay to find ultimate gain and period of linear plant. Saturation nonlinear element reduces the high harmonics of plant output. The reduction of high harmonics improve the accuracy of describing function method used to find ultimate gain and period. We give a simple procedure to find ultimate gain and period with saturation nonlinear element. A PID controller design method with known time delay element is also given, which is very useful when oscillation is not occurred with nonlinear element.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.965-969
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• 1993

In this paper, the GPC algorithm is developed for ACS(advanced control system). ACS equals to DCS(distributed control system) with some advanced control algorithm, for example, fuzzy logic controller, autotuning. By its embedded structural control language, which uses simple function codes corresponding to each function blocks, it is possible to construct multiloop controller. The developed GPC function code is divided by RLS (recursive least square) parameter estimator and GPC controller. Simulation result show the availability of GPC function code using the control language.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.123-128
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• 1992

This paper presents an autotuning algorithm of scaling factor in order to improve system performance. We define the scaling factor of fuzzy controller as a function of error and error change. This function is tuned by the output of performance evaluation level utilizing the error of overshoot and rising time. Simulation results show that the proposed algorithm has good tuning performance for a system with parameter change.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1903-1910
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• 2012

This paper presents a data based low-order controller design algorithm for a linear time-invariant process with a time delay. The algorithm is composed by combining an identification step based on open loop pulse test with a low-order controller design step to obtain the entire set of controllers achieving multiple performance specifications. The initial information necessary for this algorithm are merely the width and amplitude of a rectangular pulse, a controller of four types (PI, PD, PID, first-order), and design objectives. Various parametric approaches that have been developed are merged in the controller design algorithm. The resulting controller set satisfying the design objectives are displayed on the 2D and 3D graphics and thus it is very easy for us to pick a controller inside the admissible set because we can check the corresponding closed-loop performances visually.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.431-432
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• 2008

플랜트의 정보를 알지 못 하는 경우 시험신호로 포화함수와 시간지연 요소를 이용하여 플랜트의 정보를 찾아내고 찾아낸 정보를 PI 제어기를 설계하는 방법을 제시하였다. 시간 지연요소를 이용함으로서 PI 제어기의 I 요소로부터 발생되는 위상 감소로 인해 발생되는 불안정성이 발생하지 않도록 플랜트의 Nyquist 그림에서 3/4 분면에 있는 한 점의 정보를 찾아내는 것이 가능하였다. 제안된 된 방법은 포화함수의 이용으로 정확성이 높고 한 개의 포화함수만을 이용하여 구조가 간단하다. 또한 모의시험을 통해 제안된 방법의 타당성을 보였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.217-220
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• 2004

Controller that is designed in this paper is form that apply PID controller about Fuzzy algorithm. Fuzzy Controller that using this paper is can speak that compensation style fuzzy controller as form to solidify action of PID controller for plant. This is not form that autotuning the each PID coefficient. We Apply and examined the response character to AGC(Automatic Generation Control) system using designed controller.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.326-328
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• 2009

we an autotuning algorithm for PI controller with unknown plant. The proposed algorithm uses a saturation function and time delay element as a test signal. Since the integral element of PI controller reduces a phase margin and amplitude margin in the closed loop system, the closed loop system could be resulted in unstable with PI controller, To avoid unstable in the closed loop system with PI controller, the proposed algorithm identifies one point information in the 3rd quadrant of Nyquist plot with a time delay element. The proposed method improves an accuracy of one point identified information with one saturation function.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.106-118
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• 1998

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.