• Title/Summary/Keyword: Model parameter tuning

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Automatic PID Controller Parameter Analyzer

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

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Self-tuning PID-controller based on GPC (GPC를 이용한 자기동조 PID 제어기)

  • 유연운;김종만;이창구;김성중
    • 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.

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Initial Value Problem and Tuning of Induction Motor parameter in Elevetor vector control (엘리베이터용 유도 전동기 벡터 제어시의 초기 시정수 및 자동 조정)

  • Park, Sang-Young
    • Proceedings of the KIEE Conference
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    • 1998.11a
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    • pp.176-178
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    • 1998
  • Recently, Control method of induction Motor is applied in full circuit model, as circular diagram. In Elevetor moderization problem, there is no circuit information. Nothing but, Motor terminal voltage and HP of motor. So, in this study, using KS induction Motor table, try to solve initial valve problem and make some implementations of manual tuning for use of automatic tuning of induction motor parameter.

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Design of a Self-tuning PID Controller for Over-damped Systems Using Neural Networks and Genetic Algorithms (신경회로망과 유전알고리즘을 이용한 과감쇠 시스템용 자기동조 PID 제어기의 설계)

  • 진강규;유성호;손영득
    • 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.

Excitation and Measurement Points Selection to Identify Structural Parameters for Model Tuning (모델보정을 위한 구조물 매개변수 규명시 가진점 .측정점의 선정)

  • Park, Nam-Gyu;Park, Yun-Sik
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.24 no.5 s.176
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    • pp.1271-1280
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    • 2000
  • A sensor placement technique to identify structural parameter was developed. Experimental results must be acquired to identify unknown dynamic characteristics of a targeting structure for the comparison between analytical model and real structure. If the experimental environment was not equipped itself properly, it can be happened that some valuable information are distorted or ill-condition can be occurred. In this work the index to determine exciting points was derived from the criterion of maximizing parameter sensitivity matrix and that to choose measurement points was from that of preserving the invariant of sensitivity matrix. This idea was applied to a compressor hull structure to verify its performance. The result shows that the selection of measurement and excitation points using suggested criteria improve the ill-conditioning problem of inverse type problems such , as model updating.

The development of an on-line self-tuning fuzzy PID controller (온라인 자기동조 퍼지 PID 제어기 개발)

  • 임형순;한진욱;김성중
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.704-707
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    • 1997
  • In this paper, we present a fuzzy logic based tuner for continuous on-line tuning of PID controllers. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a singler parameter .alpha., then to use an on line fuzzy logic to self-tune the parameter. The adaptive scaling makes the controller robust against large variations in parametric and dynamics uncertainties in the plant model. New self-tuning controller has the ability to decide when to use PI or PID control by extracting process dynamics from relay experiments. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with nonminimum phase processes.

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Tuning Rules of the PID Controller Based on Genetic Algorithms (유전알고리즘에 기초한 PID 제어기의 동조규칙)

  • Kim, Do-Eung;Jin, Gang-Gyoo
    • Proceedings of the KIEE Conference
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    • 2002.07d
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    • pp.2167-2170
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    • 2002
  • In this paper, model-based tuning rules of the PID controller are proposed incorporating with genetic algorithms. Three sets of optimal PID parameters for set-point tracking are obtained based on the first-order time delay model and a genetic algorithm as a optimization tool which minimizes performance indices(IAE, ISE and ITAE). Then tuning rules are derived using the tuned parameter sets, potential rule models and a genetic algorithm. Simulation is carried out to verify the effectiveness of the proposed rules.

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Self-Organizing Fuzzy Modeling Based on Hyperplane-Shaped Clusters (다차원 평면 클러스터를 이용한 자기 구성 퍼지 모델링)

  • Koh, Taek-Beom
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.12
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    • pp.985-992
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    • 2001
  • This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

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Self-tuning optimal control of an active suspension using a neural network

  • Lee, Byung-Yun;Kim, Wan-Il;Won, Sangchul
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.295-298
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    • 1996
  • In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

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An alternative method for estimating lognormal means

  • Kwon, Yeil
    • Communications for Statistical Applications and Methods
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    • v.28 no.4
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    • pp.351-368
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    • 2021
  • For a probabilistic model with positively skewed data, a lognormal distribution is one of the key distributions that play a critical role. Several lognormal models can be found in various areas, such as medical science, engineering, and finance. In this paper, we propose a new estimator for a lognormal mean and depict the performance of the proposed estimator in terms of the relative mean squared error (RMSE) compared with Shen's estimator (Shen et al., 2006), which is considered the best estimator among the existing methods. The proposed estimator includes a tuning parameter. By finding the optimal value of the tuning parameter, we can improve the average performance of the proposed estimator over the typical range of σ2. The bias reduction of the proposed estimator tends to exceed the increased variance, and it results in a smaller RMSE than Shen's estimator. A numerical study reveals that the proposed estimator has performance comparable with Shen's estimator when σ2 is small and exhibits a meaningful decrease in the RMSE under moderate and large σ2 values.