Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5005434 | ISA Transactions | 2009 | 11 Pages |
Abstract
State estimation is a major problem in industrial systems. To this end, Gaussian and nonparametric filters have been developed. In this paper the Kalman Filter, which assumes Gaussian measurement noise, is compared to the Particle Filter, which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a DC motor is used. The reconstructed state vector is used in a feedback control loop to generate the control input of the DC motor. In simulation tests it was observed that for a large number of particles the Particle Filter could succeed in accurately estimating the motor's state vector, but at the same time it required higher computational effort.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Gerasimos G. Rigatos,