Title :
Nonlinear estimation of stator winding resistance in a brushless DC motor
Author :
Wanlin Zhang ; Gadsden, S. Andrew ; Habibi, Saeid R.
Author_Institution :
Dept. of Mech. Eng., McMaster Univ., Hamilton, ON, Canada
Abstract :
Estimation of stator winding resistance in brushless DC motors is important for fault detection and diagnosis. The most popular linear estimation method to date remains the Kalman filter (KF), and the extended form (EKF) for nonlinear systems and measurements. However, a relatively new method referred to as the smooth variable structure filter (SVSF) was introduced in an effort to overcome some of the instability issues with the KF. Further to this development, a new nonlinear estimation strategy was created based on combining elements of the EKF with the SVSF. This new method, referred to as the EK-SVSF, has been applied to a brushless DC motor for estimating the stator winding values. The results are compared with the popular EKF.
Keywords :
Kalman filters; brushless DC motors; fault diagnosis; machine windings; stators; EK-SVSF; brushless DC motor; extended Kalman filter; fault detection; fault diagnosis; instability issues; nonlinear estimation; nonlinear estimation strategy; nonlinear systems; smooth variable structure filter; stator winding resistance; stator winding resistance estimation; stator winding values; Brushless DC motors; Covariance matrices; Estimation; Mathematical model; Resistance; Uncertainty; Windings;
Conference_Titel :
American Control Conference (ACC), 2013
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4799-0177-7
DOI :
10.1109/ACC.2013.6580564
