Yazar "Inan R." seçeneğine göre listele

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    Adaptive Fading Extended Kalman Filter Based Speed-Sensorless Induction Motor Drive
    (Institute of Electrical and Electronics Engineers Inc., 2018) Zerdali E.; Yildiz R.; Inan R.; Demir R.; Barut M.
    This paper presents an adaptive fading extended Kalman filter (AFEKF) based speed-sensorless induction motor (IM) drive. Conventional extended Kalman filters (CEKFs) assume the system (Q) and the measurement (R) noise covariance matrices as constant, but those matrices are affected by the operating conditions of IMs and deteriorate the estimation performance. To eliminate this adverse effect, an AFEKF algorithm which has the ability to update Q and R matrices according to the operating conditions of IM are proposed, and the stator stationary axis components of stator currents, the stator stationary axis components of rotor fluxes, the rotor mechanical speed, and the load torque including viscous friction term are estimated. To illustrate the superiority of AFEKF-based speed-sensorless IM drive, the control performance of the proposed drive system is compared to that of CEKF-based speed-sensorless drive system under simulations. In addition to the comparison results, the computational burdens of AFEKF and CEKF algorithms are also examined. © 2018 IEEE.
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    FPGA implementation of extended Kalman filter for speed-sensorless control of induction motors
    (Institution of Engineering and Technology, 2014) Inan R.; Barut M.; Karakaya F.
    This paper presents a hardware in the loop (HIL) system including the implementation of an Extended Kalman Filter (EKF) based estimator on the Field Programmable Gate Array (FPGA) for speed-sensorless control of IM. The implemented EKF algorithm simultaneously estimates stator currents (i sa and isß), stator fluxes (?s? and ?sß), rotor angular velocity (?m), and load torque (tL) by assuming that stator voltages and currents are available. The HIL system also includes stator currents and fluxes based IM model which provides actual stator currents to the EKF algorithm and is also utilized to validate the flux, speed and load torque estimations of the implemented EKF algorithm. Virtex-5 VSX110T FPGA evolution board is used for this real-time application. The FPGA board is programmed via Very High Speed Integrated Circuit Hardware Description Language (VHDL) in order to develop both IM model and the EKF algorithm. ISE Design Suit Interface is used as debugger and compiler. The results obtained from the EKF and IM model developed on FPGA are graphically compared to verify the sufficiency of estimation performance of the EKF algorithm and demonstrate that EKF algorithm is implemented successfully with less computational time (less sampling time for each recursive operation) due to the inherent parallel signal processing ability of FPGA.
  • Küçük Resim Yok
    Öğe
    FPGA implementation of extended Kalman Filter for speedsensorless control of induction motors
    (Institution of Engineering and Technology, 2014) Inan R.; Barut M.; Karakaya F.
    This paper presents a hardware in the loop (HIL) system including the implementation of an Extended Kalman Filter (EKF) based estimator on the Field Programmable Gate Array (FPGA) for speed-sensorless control of IM. The implemented EKF algorithm simultaneously estimates stator currents (is? and isß), stator fluxes (?s? and ?sß), rotor angular velocity (?m), and load torque (tL) by assuming that stator voltages and currents are available. The HIL system also includes stator currents and fluxes based IM model which provides actual stator currents to the EKF algorithm and is also utilized to validate the flux, speed and load torque estimations of the implemented EKF algorithm. Virtex-5 VSX110T FPGA evolution board is used for this real-time application. The FPGA board is programmed via Very High Speed Integrated Circuit Hardware Description Language (VHDL) in order to develop both IM model and the EKF algorithm. ISE Design Suit Interface is used as debugger and compiler. The results obtained from the EKF and IM model developed on FPGA are graphically compared to verify the sufficiency of estimation performance of the EKF algorithm and demonstrate that EKF algorithm is implemented successfully with less computational time (less sampling time for each recursive operation) due to the inherent parallel signal processing ability of FPGA.