TY - GEN
T1 - Extended Kalman Filter based Encoderless Predictive Current Control for Induction Machine Drives
AU - Xie, Haotian
AU - Wang, Fengxiang
AU - He, Yingjie
AU - Tang, Ying
AU - Rodriguez, Jose
AU - Kennel, Ralph
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/24
Y1 - 2021/5/24
N2 - Compared with the conventional strategies for electrical drives such as Field Oriented Control (FOC), Predictive Current Control (PCC) shows the superiority of fast dynamic response and low switching frequency. Instead of an encoder, a speed-sensorless algorithm is applied to provide the rotor position for PCC, which reduces the hardware cost and complexity. However, the estimation accuracy is significantly penalized by the parameter deviation, which leads to unsatisfactory control performance like high torque and current ripples. To cope with this problem, an Extended Kalman Filter (EKF) is presented to estimate stator flux and rotor speed in this paper, which improves robustness to parameter variations and achieves high control performance. The steady-state, dynamic transient performance and robustness evaluation of the proposed scheme are experimentally validated on the 2.2kW induction machine (IM) platform. It is indicated that the proposed method shows excellent speed tracking ability and strong robustness to parameter deviations.
AB - Compared with the conventional strategies for electrical drives such as Field Oriented Control (FOC), Predictive Current Control (PCC) shows the superiority of fast dynamic response and low switching frequency. Instead of an encoder, a speed-sensorless algorithm is applied to provide the rotor position for PCC, which reduces the hardware cost and complexity. However, the estimation accuracy is significantly penalized by the parameter deviation, which leads to unsatisfactory control performance like high torque and current ripples. To cope with this problem, an Extended Kalman Filter (EKF) is presented to estimate stator flux and rotor speed in this paper, which improves robustness to parameter variations and achieves high control performance. The steady-state, dynamic transient performance and robustness evaluation of the proposed scheme are experimentally validated on the 2.2kW induction machine (IM) platform. It is indicated that the proposed method shows excellent speed tracking ability and strong robustness to parameter deviations.
KW - extended Kalman filter
KW - induction machine
KW - predictive current control
KW - robustness improvement
UR - http://www.scopus.com/inward/record.url?scp=85114212664&partnerID=8YFLogxK
U2 - 10.1109/ECCE-Asia49820.2021.9479124
DO - 10.1109/ECCE-Asia49820.2021.9479124
M3 - Conference contribution
AN - SCOPUS:85114212664
T3 - Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
SP - 865
EP - 868
BT - Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
Y2 - 24 May 2021 through 27 May 2021
ER -