• Title/Summary/Keyword: adaptive FNN controller

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Efficiency optimization control of SynRM using ALM-FNN controller (ALM-FNN 제어기를 이용한 SynRM의 효율 최적화 제어)

  • Park, Byung-Sang;Park, Ki-Tae;Ko, Jae-Sub;Choi, Jung-Sik;Chung, Dong-Hwa
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2007.05a
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    • pp.306-310
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    • 2007
  • This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism-fuzzy neural networks(ALM-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

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Maximum Torque Control of IPMSM Drive with LM-FNN Controller (LM-FNN 제어기에 의한 IPMSM 드라이브의 최대토크 제어)

  • Nam, Su-Myeong;Ko, Jae-Sub;Choi, Jung-Sik;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.566-569
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    • 2005
  • Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

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Maximum Torque Control of SynRM Drive with Artificial Intelligent Controller (인공지능 제어기에 의한 SynRM 드라이브의 최대토크 제어)

  • Ko, Jae-Sub;Choi, Jung-Sik;Kim, Kil-Bong;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2006.10c
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    • pp.257-259
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    • 2006
  • The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $^{i}d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

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Efficiency Optimization Control of SynRM Drive using Adative FNN Controller for (적응 FNN 제어기를 이용한 SynRM 드라이브의 효율 최적화 제어)

  • Choi, Jung-Sik;Lee, Jung-Chul;Lee, Hong-Gyun;Nam, Su-Myeong;Ko, Jae-Sub;Kim, Jong-Kwan;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2005.07b
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    • pp.1459-1461
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    • 2005
  • This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on fuzzy-neural networks(FNN) controller that is implemented using fuzzy control and neural networks. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. Simulation results are presented to show the validity of the proposed algorithm

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Development of Self Tuning and Adaptive Fuzzy Controller to control of Induction Motor (유도전동기 드라이브의 제어를 위한 자기동조 및 적응 퍼지제어기 개발)

  • Ko, Jae-Sub;Choi, Jung-Sik;Chung, Dong-Hwa
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.24 no.4
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    • pp.33-42
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    • 2010
  • The induction motor drive applied to field oriented control is widely used in industry applications. However, it is deceased performance and authenticity by saturation, temperature changing, disturbance and parameters changing because modeling of induction motor is nonlinear and complex. In order to control variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation, even under ideal field oriented conditions. This paper proposes self tuning PI controller based on fuzzy-neural network(FNN)-PI controller that is implemented using fuzzy control, neural network, and adaptive fuzzy controller(AFC). Also, this paper proposes estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FNN-PI, AFC and ANN controller. Also, this paper proposes the anlysis results to verify the effectiveness of controller.

HIPI Controller of IPMSM Drive using ALM-FNN (ALM-FNN을 이용한 IPMSM 드라이브의 HIPI 제어기)

  • Ko, Jae-Sub;Choi, Jung-Sik;Chung, Dong-Hwa
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.23 no.8
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    • pp.57-66
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    • 2009
  • The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper proposes hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme, The validity of the proposed controller is verified by results at different dynamic operating conditions.

on-line Modeling of Nonlinear Process Systems using the Adaptive Fuzzy-neural Networks (적응퍼지-뉴럴네트워크를 이용한 비선형 공정의 온-라인 모델링)

  • 오성권;박병준;박춘성
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.10
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    • pp.1293-1302
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    • 1999
  • In this paper, an on-line process scheme is presented for implementation of a intelligent on-line modeling of nonlinear complex system. The proposed on-line process scheme is composed of FNN-based model algorithm and PLC-based simulator, Here, an adaptive fuzzy-neural networks and HCM(Hard C-Means) clustering method are used as an intelligent identification algorithm for on-line modeling. The adaptive fuzzy-neural networks consists of two distinct modifiable sturctures such as the premise and the consequence part. The parameters of two structures are adapted by a combined hybrid learning algorithm of gradient decent method and least square method. Also we design an interface S/W between PLC(Proguammable Logic Controller) and main PC computer, and construct a monitoring and control simulator for real process system. Accordingly the on-line identification algorithm and interface S/W are used to obtain the on-line FNN model structure and to accomplish the on-line modeling. And using some I/O data gathered partly in the field(plant), computer simulation is carried out to evaluate the performance of FNN model structure generated by the on-line identification algorithm. This simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

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HIPI Controller of IPMSM Drive using ALM-FNN Control (적응학습 퍼지뉴로 제어를 이용한 IPMSM 드라이브의 HIPI 제어기)

  • Kim, Do-Yeon;Ko, Jae-Sub;Choi, Jung-Sik;Jung, Chul-Ho;Jung, Byung-Jin;Chung, Dong-Hwa
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2009.05a
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    • pp.420-423
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    • 2009
  • The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

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High Performance Speed and Current Control of SynRM Drive with ALM-FNN and FLC Controller (ALM-FNN 및 FLC 제어기에 의한 SynRM 드라이브의 고성능 속도와 전류제어)

  • Choi, Jung-Sik;Ko, Jae-Sub;Chung, Dong-Hwa
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.58 no.3
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    • pp.249-256
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    • 2009
  • The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

High Performance Speed and Current Control of SynRM Drive with ALM-FNN and FLC Controller (ALM-FNN 및 FLC 제어기에 의한 SynRM 드라이브의 고성능 속도와 전류제어)

  • Jung, Byung-Jin;Ko, Jae-Sub;Choi, Jung-Sik;Jung, Chul-Ho;Kim, Do-Yeon;Chung, Dong-Hwa
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2009.05a
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    • pp.416-419
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    • 2009
  • The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

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