Estimation and control of industrial processes with particle filters

Author

Morales-Menendez, Ruben ; De Freitas, Nando ; Poole, David

Author_Institution

Mechatronics & Autom. Dept., ITESM, Monterrey, Mexico

Volume

1

fYear

2003

fDate

4-6 June 2003

Firstpage

579

Abstract

We present a probabilistic approach to state estimation and control of industrial processes. In particular, we adopt a jump Markov linear Gaussian (JMLG) model to describe an industrial heat exchanger. The parameters of this model are identified with the expectation maximisation (EM) algorithm. After identification, particle filtering algorithms are adopted to diagnose, in real-time, the state of operation of the heat exchanger. The particle filtering estimates are then used to drive an automatic control system.

Keywords

Gaussian processes; Markov processes; filtering theory; heat exchangers; optimisation; probability; process control; state estimation; Markov linear Gaussian model; automatic control system; expectation maximisation algorithm; identification; industrial heat exchanger; industrial process control; particle filtering algorithms; particle filtering estimation; particle filters; probabilistic approach; real time operation; state estimation; Automatic control; Control systems; Electrical equipment industry; Humans; Industrial control; Instruments; Particle measurements; Process control; State estimation; Temperature control;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2003. Proceedings of the 2003

ISSN

0743-1619

Print_ISBN

0-7803-7896-2

Type

conf

DOI

10.1109/ACC.2003.1239081

Filename

1239081