• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.411-419
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• 1998

In this paper the steady-state genetic algorithm is applied for the optimal design of fuzzy PID controllers. Basically the structure of the discussed fuzzy PID controller is extended from the conventional fuzzy PI and PD controllers where only a two-dimensional rule base of the fuzzy PID controller are designed simultaneously. Simulations results shows the superior performance of this optimal designed fuzzy PID controllers to the optimal designed conventional fuzzy PI and PD controllers.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.102-109
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• 2014

In this paper, an algorithm to design fuzzy PID controllers is proposed. The proposed controllers are composed of fuzzy rules of which consequences are linear PID controllers and are designed with help of TSK fuzzy controllers. TSK fuzzy controllers are designed from TSK fuzzy model using pole assignment and have outstanding ability making the output response of nonlinear systems similar to the desired one. However, because of its structure complexity the TSK fuzzy controller is difficult to be used in industry. The proposed controllers have PID controller structure which can be easily realized, and are designed by using the data obtained from control simulations with TSK fuzzy controllers. To verify the proposed algorithm, two example simulations are performed.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1392-1395
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• 1996

Nonlinear PID controllers have increasingly used in current industrial practice because it is robust and is easy to operate. Little guideline and tuning method, however, has been recommended for the nonlinear PID controllers while a lot of result is available for the linear PID controllers. Application guideline and tuning formulae are presented for error square type nonlinear controllers, which are most popular currently, are presented.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.362-366
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• 1986

Using simple linear prediction algorithm a design procedure of robust PI and PID controllers for SISO system, usually called 'PID predictor controllers, is developed. The design procedure is able to properly adjust gain margin and phase margin and control coefficients are selected in frequency domain. The performance of the PID predictor controller is superior to that of the normal PID controller in terms of robustness in design and disturbance rejection.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.212-218
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• 2021

The Proportional Integral Derivative (PID) controller is the most popular industrial controller and more than 90% process industries use this controller. During the past 50 years, numerous good tuning methods have been proposed for Single Input Single Output Systems. However, design of PI/PID controllers for multivariable processes is a challenge for the researchers. A comparative study of three PID controllers design methods has been carried-out. These methods include the DS (Direct Synthesis) method, IMC (Internal model Control) method and ETF (Effective Transfer Function) method. MIMO PID controllers are designed for a number of 2×2, 3×3 and 4×4 process models with multiple delays. The performance of the three methods has been evaluated through simulation studies in Matlab/Simulink environment. After extensive simulation studies, it is found that the Effective Transfer Function (ETF) Method produces better output responses among two methods. In this work, only decentralized methods of PID controllers have been studied and investigated.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.11-18
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• 2021

The Proportional Integral Derivative (PID) controller is the most popular industrial controller and more than 90% process industries use this controller. During the past 50 years, numerous good tuning methods have been proposed for Single Input Single Output Systems. However, design of PI/PID controllers for multivariable processes is a challenge for the researchers. A comparative study of three PID controllers design methods has been carried-out. These methods include the DS (Direct Synthesis) method, IMC (Internal model Control) method and ETF (Effective Transfer Function) method. MIMO PID controllers are designed for a number of 2×2, 3×3 and 4×4 process models with multiple delays. The performance of the three methods has been evaluated through simulation studies in Matlab/Simulink environment. After extensive simulation studies, it is found that the Effective Transfer Function (ETF) Method produces better output responses among two methods. In this work, only decentralized methods of PID controllers have been studied and investigated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1383-1391
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• 2016

This paper is concerned with applicability of a new type of controllers, called i-PID and GPI in which unknown parts of the plant are taken into account without any modeling procedure, to automatic voltage regulator (AVR) system. First, the procedure for applying i-PID and GPI algorithms to AVR system is proposed, which uses model reduction technique based on the given information of AVR. Second, simulations are given to verify their effectiveness comparing to various PID algorithms including PIDD2 which is four-term controller, that is, consisting of PID and second order derivative terms. Superior response performances of i-PID and GPI in comparison to conventional PID controllers are shown. Moreover, i-PID can highly improve the system robustness with respect to model uncertainties, especially to load variations.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.9
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• pp.750-754
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• 2005

This paper deals with the design problem of multivariable PID controllers guaranteeing the closed-loop system stability and a prescribed $H_\infty$ norm bound constraint. We reduce the problem to the static output feedback stabilization problem. We derive a necessary and sufficient condition f3r the existence of PID controllers and we give an explicit formula of PID controllers. We also give an existence condition of PID controllers guaranteeing a prescribed decay rate. Finally, we give an LMI-based design algorithm, together with a numerical design example.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1008-1011
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• 1996

Most control systems of power plants are using classical PID controllers for their process control. In order to get the desired control performances, the correct tuning of PID controllers is very important. Sometimes, it is necessary to retune PID controllers after the change of system operating condition and system design change, etc. Commercial auto-tuning controllers such as relay feedback controller can be used for this purpose. However, using these controllers to the safety-critical systems of nuclear power plants may be cause of unsafe operation, because they are using test signals for tuning. A new system identification auto-tuning method without using test signal has been developed in this paper. This method uses process input/output signals for system identification of unknown control process. From the model information of control process which was obtained from system identification approach, the optimal PID parameters can be calculated. The method can be used in the safety-critical systems because it is not using test signals during system modeling process.