Speed sensorless control strategy for six‐phase linear induction motor based on the dual reduced‐dimensional serial extended Kalman filters

Abstract

This study proposes a speed sensorless vector control strategy for the six-phase linear induction motor (SPLIM) based on the dual reduced-dimensional serial extended Kalman filters (DRDSEKFs). Firstly, the low-order mathematical model of SPLIM is obtained according to the equivalent transformation of primary voltage, current and flux components in the stationary coordinate system. Then, the state equation is derived and the speed estimation based on the five-dimensional extended Kalman filter (EKF) is realised. To reduce the computation cost and improve the accuracy of speed estimation, a DRDSEKFs algorithm is proposed, with one two-dimensional EKF and one three-dimensional EKF operating serially in every control period. As the real-time performance of the algorithm is guaranteed, it is possible to overcome the hysteretic nature of the traditional EKF and improve the dynamic estimation performance. Simulations and experiments show that the proposed sensorless control strategy is feasible and effective.

Sensorless Direct Torque Control of the Six-Phase Induction Motor by Fast Reduced Order Extended Kalman Filter

“…Other estimators are used for parameter estimation of induction machine parameters [30][31][32][33]. EKF is used to model a sixphase induction machine with powerful results [34,35].…”

Real-Time Implementation of Extended Kalman Filter Observer With Improved Speed Estimation for Sensorless Control

“…The performance of improved EKF is tested using hardware setup built as in Fig. 14 for different scenarios of variations in load torque, changes in speed command, no-load conditions, and the worst case of zero speed state [14]. Hardware-in-loop is done by inserting the observer in the controller scheme in the Simulink environment.…”

“…Various methods have been proposed over the last few decades for speed estimation and control of IM. Conventional speed estimation techniques include the MRAS [8], derivatives of rotor flux [9 ]- [10], voltages in stator [11], a modified version of stator model, full order observer [11]- [12], unscented extended Kalman filter [13], reduced-order nonlinear observer [14]- [15], Extended Kalman filter observer assisted with fuzzy optimization [16], sliding mode observer [17]- [18]. Few other speed estimators that don't depend on measured values of voltage and current are Artificial intelligent (AI) based neural networks [19]- [20], harmonic rotor slotting [21]- [22], high-frequency signal injection [21]- [23].…”