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

被引：46
|
作者：
Rigatos, Gerasimos G. [1 ]
机构：
[1] Ind Syst Inst, Unit Ind Automat, Rion 26504, Greece
来源：
ISA TRANSACTIONS | 2009年 / 48卷 / 01期
关键词：
DC motor; Sensorless control; Nonparametric filters; Kalman filter; Particle filter; NEURAL-NETWORK CONTROLLER; NONLINEAR-SYSTEMS; MOTION CONTROL; SENSOR FUSION; TRACKING; IDENTIFICATION; OBSERVER; STABILIZATION; ALGORITHMS;
D O I：
10.1016/j.isatra.2008.10.005
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. (C) 2008 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：62 / 72
页数：11
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