• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.10 no.1
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• pp.1-6
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• 2014

PID(Proportional, Integral, Derivative) controller is one of the most used process controllers in nuclear power plants. The optimized parameter setting of process controller contributes to the stable operation and efficiency in the operating nuclear power plants. PID parameter setting is tuned when new process control system is established or process control system is changed. It is a burdensome work for I&C(Instrument and Control) engineers to tune the PID controller because it requires a lot of experience and knowledge. When the plant is in operation, inadequate PID parameter setting can be the cause of the unstable process of the plant. Therefore the results of PID parameter setting should be compared, simulated, verified and finally optimized. The practical PID tuning methods used in process controller are tuning operation calculation(Ziegler-Nicholes, Minimum TIAE, Lambda, IMC), exclusive tuning program based on computer and Matlab application. This paper introduces the various tuning methods and suggests an optimized PID tuning process in the operating nuclear power plants.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1077-1080
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• 1991

It has been recognized as important subject by users that PID-Controllers widely used in industrial processes must be well-tuned, In this paper, We present an automatic tuning method for PID-Controllers which is based on discrete parameter estimation and application of conventional tuning-rules. The method is easy to implement on microprocessor because critical values are obtained by the mathematical computation. Also, it permits quick on-line tuning. Simulation results show that most processes are well tuned by the suggested tuning method in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.188-193
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• 1992

The PID controllers which is widely used in the process industry are poorly damped when the dynamic process contains significant dead time or when there are random disturbances acting on the plant. GPC is known to be more superior than conventional self-tuning algorithm in overcoming above problem and prior choice of model order. In this paper, we propose the method which determine the parameter of PID controller from minimization of GPC criterion. The controller has emplicit scheme which is comprised of parameter estimation and PID control design. Simulation results show the performance of the proposed self-tuning PID controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.95-103
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• 2001

The purpose of Introducing a combined cycle with gas turbine in power plants is to reduce losses of energy, by effectively using exhaust gases from the gas turbine to produce additional electricity or process. The efficiency of a combined power plant with the gas turbine increases, exceeding 50%, while the efficiency of traditional steam turbine plants is approximately 35% to 40%. Up to the present time, the PID controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain without any experience, since the gain of the PID controller has to be manually tuned by trial and error procedures. This paper focuses on the neural network tuning of the 2-DOF PID controller with a combined 2-DOF parameter (NN-Tuning 2-DOF PID controller), for optimal control of the Gun-san gas turbine generating plant in Seoul, Korea. In order to attain optimal control, transfer function and operating data from start-up, running, and stop procedures of the Gun-san gas turbine have been acquired and a designed controller has been applied to this system. The results of the NN-Tuning 2-DOF PID are compared with the PID controller and the conventional 2-DOF PID controller tuned by the Ziegler-Nichols method through experimentation. The experimental results of the NN-Tuning 2-DOF PID controller represent a more satisfactory response than those of the previously-mentioned two controllers.

• 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 Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.644-649
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• 2006

In this paper, a methodology for estimating the model parameters of a discrete-time system and tuning a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems regarding parameter estimation and controller tuning, pseudo-parallel genetic algorithms(PPGAs) are used. The parameters of a discrete-time system are estimated using both the model adjustment technique and a PPGA. The digital PID controller is described by the pulse transfer function and then its three gains are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.288-291
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• 1999

The PID (Proportional-Integral-Derivative) controller is widely used in the industries for more than fifty years with the well known Ziegler-Nichols tuning method and others varieties. However, most of the PID controller being used in the real practice still require trial and error adjustment for each process after the tuning method is done, which is consuming of time and needs the operator experiences to obtain the best results for the controller parameter. In order to reduce the inconvenience in the controller tuning, this paper presents a design of an automatic PID controller parameter analyzer being used as a support instrument in the industrial process control. This analyzer is designed based on the tuning formula of Dahlin to synthesize the PID controller parameter. Using this analyzer, the time to be spent in the trial and error procedures and its complexity can be neglected. Experimental results using PID controller parameter synthesized from this analyzer to the liquid level control plant model and the fluid flow control plant model show that the responses of the controlled systems can be efficiently controlled without any difficulty in mathemathical computation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1371-1376
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• 2003

In the thermal power plant, the main steam temperature is typically regulated by the fuel flow rate and the spray flow rate, and the reheater steam temperature is regulated by the gas recirculation flow rate. However, Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature, the change of the dynamic characteristics in the reheater. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on tuning of the 2-DOF PID Controller on the DCS for steam temperature control using immune based multiobjective approach. The stable range of a 2-DOF parameter for only this system could be found for the start-up procedure and this parameter could be used for the tuning problem. Therefore tuning technique of multiobjective based on immune network algorithms in this paper can be used effectively in tuning 2-DOF PID controllers.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.52-57
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• 2003

A PID parameter tuning method was presented by the fuzzy singleton inference, based on step response-shaping of plant and experience knowledge of expert. The parameter-tuning has tow levels. The higher level determines modified coefficients for the controller based on operator's tuning know-how for characteristics of plant which can not be modeled. The lower level determines specified coefficients based on characteristics of response by Ziegler-Nickel's bounded sensitivity method. The last level parameters tuning of a PID controller is adjusted which the modified and specified coefficients makes adjustment rule, and is adjusted the proper value to each parameters by fuzzy singleton inference. Moreover, proposed the tuning method can reflex exporter knowledge and operator's tuning know-how and fuzzy singleton inference is rapidly operated.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.24-32
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• 2003

The PID controller has been widely used in industrial applications due to its simple structure and robustness. Even if it is initially well tuned, the PID controller must be retuned to maintain acceptable performance when there are system parameter changes due to the change of operation conditions. In this paper, a self-tuning control scheme which comprises a parameter estimator, a NN-based rule emulator and a PID controller is proposed, which can cope with changing environments. This method involves combining neural networks and real-coded genetic algorithms(RCGAs) with conventional approaches to provide a stable and satisfactory response. A RCGA-based parameter estimation method is first described to obtain the first-order with time delay model from over-damped high-order systems. Then, a set of optimum PID parameters are calculated based on the estimated model such that they cover the entire spectrum of system operations and an optimum tuning rule is trained with a BP-based neural network. A set of simulation works on systems with time delay are carried out to demonstrate the effectiveness of the proposed method.