• Title, Summary, Keyword: Controller parameter tuning

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A Method of Tuning Optimization for PID Controller in Nuclear Power Plants (원자력발전소 PID 공정제어기에 대한 튜닝 최적화 방법)

  • Sung, Chan Ho;Min, Moon Gi
    • 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.

Automatic PID Controller Parameter Analyzer

  • Pannil, Pittaya;Julsereewong, Prasit;Ukakimaparn, Prapart;Tirasesth, Kitti
    • 제어로봇시스템학회:학술대회논문집
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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.

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Application of Personal Computer as a Self-Tuning PID Controller

  • Tanachaikhan, L.;Sriratana, W.;Pannil, P.;Chaikla, A.;Julsereewong, P.;Tirassesth, K.
    • 제어로봇시스템학회:학술대회논문집
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    • pp.505-505
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    • 2000
  • Controlling the process by PID controller is widely used in industry by applying Ziegler-Nichols method in analyzing parameter of the controller. However, in fact. it is still necessary to tune parameter in order to obtain the best process response. This paper presents a Self-Tuning PID controller utilizes the personal computer to synthesize and analyze controller parameter as well as tune for appropriate parameter by using Dahlin method and Extrapolation. Experimental results using a Self-Tuning PID controller to control water level and temperature, it is found that the controller being developed is able to control the process very effectively and provides a good response similar to the controller used in the industry.

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Neural Network Tuning of the 2-DOF PID Controller With a Combined 2-DOF Parameter For a Gas Turbine Generating Plant

  • Kim, Dong-Hwa
    • 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.

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System Parameter Estimation and PID Controller Tuning Based on PPGAs (PPGA 기반의 시스템 파라미터 추정과 PID 제어기 동조)

  • Shin Myung-Ho;Kim Min-Jeong;Lee Yun-Hyung;So Myung-Ok;Jin Gang-Gyoo
    • 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.

A Simulation Method of PID Tuning with Process Modeling in Operating Nuclear Power Plants (가동원전에서 공정모델링을 통한 PID 튜닝 시뮬레이션 방법)

  • Min, Moon-Gi;Jung, Chang-Gyu;Lee, Kwang-Hyun;Lee, Jae-Ki;Kim, Hee-Je
    • 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.

Design of a Direct Self-tuning Controller Using Neural Network (신경회로망을 이용한 직접 자기동조제어기의 설계)

  • 조원철;이인수
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.40 no.4
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    • pp.264-274
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    • 2003
  • This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

Real-time Fuzzy Tuned PID Control Algorithm (실시간 퍼지 동조 PID 제어 알고리즘)

  • Choi Jeong-Nae;Oh Sung-Kwun;Hwang Hyung-Soo
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.423-426
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    • 2005
  • In this paper, we proposed a PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kp, $\tau_{I}$, $\tau_{D}$. by the Ziegler-Nichols formula that uses the ultimate gain and ultimate period from a relay tuning experiment. We will get the error and the error rate of plant output corresponding to the initial value of parameter and fnd the new proportion gain(Kp) and the integral time ($\tau_{I}$) from fuzzy tuner by the error and error rate of plant oueut as a membership function of fuzzy theory. This fuzzy auto tuning algorithm for PID controller considerably reduced the overshoot and rise time as compared to any other PID controller tuning algorithms. And in real parametric uncertainty systems, it constitutes an appreciable improvement of performance. The significant property of this algorithm is shown by simulation

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An Enhanced Technologies of Intelligent HVAC PID Controller by Parameter Tuning based on Machine Learning

  • Kim, Jee Hyun;Cho, Young Im
    • Journal of the Korea Society of Computer and Information
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    • v.22 no.12
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    • pp.27-34
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    • 2017
  • Design of an intelligent controller for efficient control in smart building is one of the effective technologies to reduce energy consumption by reducing response time with keeping comfortable level for inhabitants. In this paper, we focus on how to find major parameters in order to enhance the ability of HVAC(heating, ventilation, air conditioning) PID controller. For the purpose of that, we use machine learning technologies for tuning HVAC devices. We show the simulation results to illustrate the behavioral relation of whole system and each control parameter while learning process.

Model-based Tuning Rules of the PID Controller Using Real-coded Genetic Algorithms (RCGA를 이용한 PID 제어기의 모델기반 동조규칙)

  • 김도응;진강규
    • Journal of Institute of Control, Robotics and Systems
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    • v.8 no.12
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    • pp.1056-1060
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    • 2002
  • Model-based tuning rules of the PID controller are proposed incorporating with real-coded genetic algorithms. The optimal parameter sets of the PID controller for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controllers, performance indices(ISE, IAE and ITAE) are adopted. Then tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm A set of simulation works is carried out to verify the effectiveness of the proposed rules.