• Title/Summary/Keyword: self-tuning filter

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A Fuzzy Self-Tuning PID Controller with a Derivative Filter for Power Control in Induction Heating Systems

  • Chakrabarti, Arijit;Chakraborty, Avijit;Sadhu, Pradip Kumar
    • Journal of Power Electronics
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    • v.17 no.6
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    • pp.1577-1586
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    • 2017
  • The Proportional-Integral-Derivative (PID) controller is still the most widespread control strategy in the industry. PID controllers have gained popularity due to their simplicity, better control performance and excellent robustness to uncertainties. This paper presents the optimal tuning of a PID controller for domestic induction heating systems with a series resonant inverter for controlling the induction heating power. The objective is to design a stable and superior control system by tuning the PID controller with a derivative filter (PIDF) through Fuzzy logic. The paper also compares the performance of the Fuzzy PIDF controller with that of a Ziegler-Nichols PID controller and a fine-tuned PID controller with a derivative filter. The system modeling and controllers are simulated in MATLAB/SIMULINK. The results obtained show the effectiveness and superiority of the proposed Fuzzy PID controller with a derivative filter.

Load Following Control of Pressurized Water Reactor (P.W.R. 원자로의 부하추종제어)

  • Lee, Buhm;Park, Young-Hwan
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.3
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    • pp.221-225
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    • 2008
  • This paper presents a self-tuning controller for pressurized water reactor (P.W.R.). This self-tuning controller includes two substantial steps, such as parameter identification and control-law building in each cycle. Extended least square algorithm is used for parameter identification, Kalman filter is used for state estimation, and discrete Riccati equation is used for optimal control. Effectiveness of this algorithm is shown through computer simulation and sensitivity analysis.

Adaptive Kalman Filter Design for an Alignment System with Unknown Sway Disturbance

  • Kim, Jong-Kwon;Woo, Gui-Aee;Cho, Kyeum-Rae
    • International Journal of Aeronautical and Space Sciences
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    • v.3 no.1
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    • pp.86-94
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    • 2002
  • The initial alignment of inertial platform for navigation system was considered. An adaptive filtering technique is developed for the system with unknown and varying sway disturbance. It is assumed that the random sway motion is the second order ARMA(Auto Regressive Moving Average) model and performed parameter identification for unknown parameters. Designed adaptive filter contain both a Kalman filter and a self-tuning filter. This filtering system can automatically adapt to varying environmental conditions. To verify the robustness of the filtering system, the computer simulation was performed with unknown and varying sway disturbance.

Load Variation Compensated Neural Network Speed Controller for Induction Motor Drives

  • Oh, Won-Seok;Cho, Kyu-Min;Kim, Young-Tae;Kim, Hee-Jun
    • KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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    • v.3B no.2
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    • pp.97-102
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    • 2003
  • In this paper, a recurrent artificial neural network (RNN) based self-tuning speed controller is proposed for the high-performance drives of induction motors. The RNN provides a nonlinear modeling of a motor drive system and could provide the controller with information regarding the load variation system noise, and parameter variation of the induction motor through the on-line estimated weights of the corresponding RNN. Thus, the proposed self-tuning controller can change the gains of the controller according to system conditions. The gain is composed with the weights of the RNN. For the on-line estimation of the RNN weights, an extended Kalman filter (EKF) algorithm is used. A self-tuning controller is designed that is adequate for the speed control of the induction motor The availability of the proposed controller is verified through MATLAB simulations and is compared with the conventional PI controller.

Load variation Compensated Neural Network Speed Controller for Induction Motor Drives (부하변동을 보상한 유도전동기 신경망 속도 제어기)

  • Oh, Won-Seok;Cho, Kyu-Min;Kim, Hee-Jun;Hyun, Sin-Tae;Kim, Young-Tae
    • Proceedings of the KIEE Conference
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    • 2002.07b
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    • pp.1137-1139
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    • 2002
  • In this paper, recurrent artificial neural network (RNN) based self tuning speed controller is proposed for the high performance drives of induction motor. RNN provides a nonlinear modeling of motor drive system and could give the information of the load variation, system noise and parameter variation of induction motor to the controller through the on-line estimated weights of corresponding RNN. Thus, proposed self tuning controller can change gains of the controller according to system conditions. The gain is composed with the weights of RNN. For the on-line estimation of the weights of RNN, extended kalman filter (EKF) algorithm is used. Self tuning controller that is adequate for the speed control of induction motor is designed. The availability of the proposed controller is verified through the MATLAB simulation with the comparison of conventional PI controller.

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Effect of Filter Parameters on a Supercontinuum-Based All-Optical Tunable Thresholder

  • Zhu, Huatao;Wang, Rong;Pu, Tao;Fang, Tao;Xiang, Peng;Zhu, Huihui
    • Journal of the Optical Society of Korea
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    • v.20 no.4
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    • pp.470-475
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    • 2016
  • In this paper, the effects of filter parameters on a supercontinuum-based all-optical thresholder are experimentally investigated. By tuning the filter parameters, the power transfer function and power transmission function are tailored. The experimental results show that a thresholder with short center wavelength has a better power function, and the slope in the middle level of the thresholder increases with increasing bandwidth. Through tuning the filter parameters, the thresholder can achieve a steplike power transfer function for optical thresholding, and a steplike power transmission function for optical self-switching. This makes the supercontinuum-based thresholder more flexible, and allows customization of performance to meet different demands in various applications.

The robustness of continuous self tuning controller for retarded system

  • Lee, Bongkuk;Huh, Uk Youl
    • 제어로봇시스템학회:학술대회논문집
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    • 1991.10b
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    • pp.1930-1933
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    • 1991
  • In this paper, the robustness of self turning controller on the continuous time-delay system is investigated. The polynomial identification method using continuous time exponentially weighted least square algorithm is used for estimating the time.-delay system parameters. The pole-zero and pole placement method are adopted for the control algorithm. On considering the control weighting factor and reliability filter the effect of unmodeled dynamics of the plant are examined by the simulation.

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The Robustness of Continuous Implicit Self Tuning Controller (연속치 내재형 자기동조 제어기의 강인성)

  • Lee, Bong-Kuk;Huh, Uk-Youl
    • Proceedings of the KIEE Conference
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    • 1990.07a
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    • pp.496-499
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    • 1990
  • In this paper, the robustness of implict self tunning controller on the continuous time system is investigated. Continuous time exponentially weighted least square algorithm is used for estimating the system parameters. The pole-zero placement method is adapted for the control algorithm. On considering the control weighting factor and realizability filter the effects of unmodeled dynamics of the plant are examined by the simulation.

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Adaptive Line Enhancer with Self-tuning Prefilter (Self-tuning 전처리필터를 이용한 적응 라인 인핸서)

  • Park, Young-Seok;Shin, Hyun-Chool;Song, Woo-Jin
    • Proceedings of the IEEK Conference
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    • 2001.09a
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    • pp.95-98
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    • 2001
  • The adaptive line enhancer (ALE) is widely used for enhancing narrowband signals corrupted by broadband noise. In this paper, we propose novel ALE methods to improve the enhancing capability. The proposed methods are motivated by the fact that the output of the ALE is a fine estimate of the desired narrowband signal with the broadband noise component suppressed. The proposed methods preprocess the input signal using ALE filter to regenerate a finer input signal. Thus the proposed ALE is driven by the input signal with higher signal-to-noise ratio (SNR). The analysis and simulation results are presented to demonstrate that the proposed ALE has better performance than conventional ALE´s.

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Algorithm of model reference adaptive control with error signal via walsh functions (Walsh 함수에 의한 신호잡음을 갖는 MRAC의 알고리즘)

  • 안두수;이재춘
    • 제어로봇시스템학회:학술대회논문집
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    • 1986.10a
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    • pp.95-96
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    • 1986
  • 시스템을 입력과 출력값 만으로 제어하고자 할 경우에는, 플랜트의 파라메타를 추정하면서 제어해 나가야 할 것이다. 이러한 경우에는, 귀환제어나 최적제어 형태로는 여러가지 문제점이 발견되어서, 최근에 적응제어가 많이 연구되고 있다. 이에는 Gain-Scheduling 방법, Self-tuning regulator 방법 및 model reference adaptive control 방법이 있다. Gain-Scheduling 방법은 미지의 파라메타가 plant에 있을지라도, 이를 즉시 예측할 수 있을 경우 보조변수 추정을 통하여 이득을 조절하여 시스템을 안정시키는 것이고, self tuning regulator는 보조변수를 직접 조정하여 시스템을 제어한다. 또 model reference adaptive control 방법은 기준모델을 정하여, 이에 따라 관측기 등을 통하여, 플랜트의 파라메타를 추정 제어해 나가는 것이다. 이때 기준 모델의 출력과 플랜트 출력사이의 오차를 어떻게 할 것인가? 추정되는 파라메타와 오차와의 대수관계 및 차수 등, 그 한계 해석이 최근의 MRAC 설계연구에 큰 과제가 되어 왔다. 이에 본 연구에서는 신호합성 및 해석에 뛰어난 기능이 있는 Walsh 함수를 이용하여, 간단한 Micro computer의 도움으로, 오차 함수를 합성하고, 미지의 파라메타를 추정하여, 시스템의 adaptive filter설계에의 가능성에 대하여 연구하고자 한다. 또 이를 실제 예를 들어 고찰하였다.

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