A robust model predictive control algorithm for stable linear plants

Author

Badgwell, Thomas A.

Author_Institution

Dept. of Chem. Eng., Rice Univ., Houston, TX, USA

Volume

3

fYear

1997

fDate

4-6 Jun 1997

Firstpage

1618

Abstract

This paper presents a new robust model predictive control (MPG) algorithm for stable, linear plants that is a direct generalization of the nominally stabilizing regulator presented by Rawlings and Muske (1993). Model uncertainty is parameterized by a list of possible plants. Robust stability is achieved through the addition of constraints that prevent the sequence of optimal controller costs from increasing for the true plant. Asymptotic stability is demonstrated through a Lyapunov argument. Simulation experiments demonstrate the performance of the algorithm for a continuous stirred tank reactor (CSTR) process

Keywords

Lyapunov methods; asymptotic stability; optimal control; predictive control; robust control; uncertain systems; CSTR; Lyapunov argument; MPG algorithm; asymptotic stability; continuous stirred tank reactor; model uncertainty; nominally stabilizing regulator; optimal controller cost sequence; robust model predictive control algorithm; robust stability; stable linear plants; Continuous-stirred tank reactor; Cost function; Optimal control; Prediction algorithms; Predictive control; Predictive models; Regulators; Robust control; Robust stability; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.610857

Filename

610857