A robustification of optimal filtered stable predictive control

Author

Ebert, Wolfram

Author_Institution

Inst. of Comput. Sci., Humboldt-Univ., Berlin, Germany

Volume

4

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

3697

Abstract

A new optimal filtered version of stable generalized predictive control is presented. Using the concept of frequency weightings an additional degree of freedom for robustness tuning is introduced. The robust stability in respect to additive plant uncertainty is proven. A final control application to a cold rolling mill in an industrial noisy environment is discussed, using the Kalman design and robustness tuning. This application example exhibits enhanced tracking accuracy and improved stability margins

Keywords

Kalman filters; control system synthesis; filtering theory; noise; optimal control; predictive control; robust control; stability criteria; uncertain systems; Kalman design; additive plant uncertainty; cold rolling mill; frequency weightings; industrial noisy environment; optimal filtered stable predictive control; robustification; robustness tuning; stability margins; stable generalized predictive control; tracking accuracy; Frequency; Industrial control; Milling machines; Predictive control; Robust control; Robust stability; Robustness; Tuning; Uncertainty; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1999. Proceedings of the 38th IEEE Conference on

ISSN

0191-2216

Print_ISBN

0-7803-5250-5

Type

conf

DOI

10.1109/CDC.1999.827928

Filename

827928