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Particle and Kalman filtering for state estimation and control of DC motors.

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.

Year of Publication2009

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 law 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 can succeed accurate estimates of the motor's state vector, but at the same time it required higher computational effort.

Citation

Rigatos, G. G. (2009) "Particle and Kalman filtering for state estimation and control of DC motors."ISA Transactions 48 (1) pp 62-72

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Additional keywords/tags

particle filtersensorless controlnonparametric filtersdc motorkalman filter
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Supporting the development of the national rural economy.

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