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Öğe 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.Öğe Bi input-extended Kalman filter based speed-sensorless direct torque control of IMs(2010) Barut M.; Demir R.This study presents bi input-extended Kalman filter (BI-EKF) based speed-sensorless direct torque control (DTC) of induction motors (IMs). For this aim, all states required for the speed-sensorless control system as well as commonly known parameter uncertainties related to stator resistance R s, rotor resistance R'r, and load torque t L including also viscous friction term have been estimated by using BI-EKF for a wide speed range. BI-EKF uses a single EKF algorithm with consecutive operation of two inputs obtained from two extended IM models developed for the simultaneous estimation of R'r and R s; thus, it has an advantage over previous EKF based studies utilizing two separate EKF algorithms for the same purpose. By assuming that stator phase voltages and currents are available, the proposed control system has been tested with the instantaneous step and/or linear variations of the velocity reference, tL, Rs, and R'r in simulations. In spite of those challenging variations, the control system performs quite well. ©2010 IEEE.Öğe Deep neural network training with iPSO algorithm [IPSO algoritmasi ile derin sinir agi egitimi](Institute of Electrical and Electronics Engineers Inc., 2018) Kosten M.M.; Barut M.; Acir N.Deep learning-based methods are frequently preferred in many areas in recent years. Another issue, which is as important as deep neural networks applications, is the training of deep neural networks. Although many techniques are proposed in the literature for the training of deep nets, most of these techniques use gradient descent based approaches. In this study, differently from the conventional gradient method, Improved Particle Swam Optimisation (IPSO) algorithm is used for the training of deep neural networks. LeNet-5 network is preferred as network structure and MNIST is utilized as data set. Depending on the number of particles, a performance of up to 96.29% was achieved. In the cases after 20 particles, the average performance was over 90%. © 2018 IEEE.Öğe 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.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.Öğe Optimization of model reference adaptive system based speed estimation for speed sensorless control of induction motors via differential evolution algorithm(IFAC Secretariat, 2013) Barut M.; Yalcin M.; Zerdali E.; Demir R.This study proposes an optimally tuned Model Reference Adaptive System (MRAS) based speed estimator using back electromotive force (EMF) vector, which does not require pure integration. The PI (Proportional and Integral) gain coefficients in the speed estimator are optimally determined by utilizing Differential Evolution (DE) algorithm. The performance of the speed estimator is tested with both simulation and real-time experiments for a wide speed range. The obtained results verify the desired performance of speed estimation. © IFAC.
