Comparison of Extended-Kalman- and Particle-Filter-Based Sensorless Speed Control

Abstract

State estimation process is one of the major concerns for controlling and monitoring systems in industry which requires high-cost measurements or unmeasurable variables of nonlinear systems. These drawbacks can be highly eliminated by designing systems without using any kind of sensors. In this paper, sensorless speed control of a dc motor was performed by using extended Kalman filter (EKF) and particle filter (PF). The speed information is estimated by using armature current data measured from a dc motor which is controlled in various speed references with a closed-loop controller. Furthermore, a performance comparison of the EKF and the PF by taking into consideration their estimation errors under the same conditions was realized in a simulation environment. The comparison results showed that the estimation performance of the PF is more accurate but slower than the EKF. The quantitative values of accurateness and slowness are depended on the particle number of the PF. The obtained computation times of the PF having ten particles and the EKF are 180 and 15 mu s, respectively.