Marginalized particle filter for sensorless control of PMSM drives

Marginalized particle filter is a stochastic filter combining Kalman filters with particle filters. It decomposes the model into linear and nonlinear part and applies the Kalman filter for the former and the particle filter for the latter. Its application in sensorless control of permanent magnet synchronous motor (PMSM) drives is based on separate treatment of the state variables: the rotor position is represented by a set of samples (particles), and the rotor speed is estimated by the Kalman filters associated with each sample. In effect, this allows to represent accurately the inherent non-Gaussianity and nonlinearity of the model. We show that the resulting filter is capable to estimate the rotor position in the full speed range, including the standstill. Analysis of the filter performance is presented on open-loop off-line analysis of data recorded on a drive prototype. Execution time of optimized implementation of the algorithm for six particles in DSP is comparable to that of the Extended Kalman filter for full state-space model. Closed-loop performance of the filter (a sensorless drive control) is evaluated on developed drive prototype of rated power of 10.7kW.