Position estimation error reduction using recursive-least-square adaptive filter for model-based sensorless interior permanent-magnet synchronous motor drives

To improve the performance of sensorless interior permanent-magnet synchronous motor (IPMSM) drives, an adaptive filter (AF) using recursive-least-square (RLS) algorithm is proposed for the electromotive force (EMF) model-based sliding-mode observer with a quadrature phase-locked loop (PLL) tracking estimator. The inverter nonlinearities and flux spatial harmonics, which cause the position estimation error with the sixth harmonic, are analyzed. An AF based on the adaptive noise-cancelling principle in cascade with a quadrature PLL is adopted to remove the harmonic estimation error. According to the harmonic characteristics of the estimation error from the quadrature PLL, the AF coefficients can be continuously updated by the RLS algorithm. The application of the RLS algorithm guarantees the fast convergence rate of the AF. Through the AF using the RLS algorithm, the harmonics of the estimated EMF can be effectively compensated. Therefore, the selected position estimation harmonic error can be eliminated. The effectiveness of the proposed method is verified with the experimental results at a 2.2-kW sensorless IPMSM drive.