Sensorless control of PMSM using an adaptively tuned SCKF

Abstract

This study reports the application of an adaptively tuned square-root Cubature Kalman filter (SCKF) for the speed and position estimation of a permanent magnet synchronous motor (PMSM) drive. The proposed estimator is observed to exhibit improved noise rejection characteristics as compared to the hitherto widely applied extended Kalman filter (EKF) observer. A third degree spherical–radial cubature rule is used in the Cubature Kalman filter (CKF) to numerically compute the multivariate moment integrals of the general Bayesian estimation equation. CKF is a non-linear filter which avoids linearisation and the associated errors. The realisation of CKF using the square-root algorithm results in numerical stability, as with the realisation of EKF using the square-root algorithm. Simulation results are presented for a three-phase inverter-fed PMSM, along with the experimental results. The estimator and the control algorithms are realised on the MATLAB real-time environment, interfaced with the hardware using the National Instruments data acquisition system NI PCI-6221.

Keywords

• numerical stability
• synchronous motor drives
• permanent magnet motors
• Kalman filters
• nonlinear filters
• Bayes methods
• sensorless machine control
• invertors
• data acquisition
• NI PCI-6221
• National Instruments data acquisition system
• square-root algorithm
• adaptively tuned square-root cubature Kalman filter
• PMSM
• sensorless control
• speed estimation
• third degree spherical-radial cubature rule
• position estimation
• adaptively tuned SCKF
• control algorithms
• three-phase inverter-fed PMSM
• numerical stability
• nonlinear filter
• general Bayesian estimation equation
• multivariate moment integrals
• CKF
• improved noise rejection characteristics
• permanent magnet synchronous motor drive