Interpolated Model Predictive Controller for Linear Systems with Bounded Disturbances

Author

Sui, D. ; Ong, C.J.

Author_Institution

Norwegian Univ. of Sci. & Technol., Trondheim

fYear

2007

fDate

9-13 July 2007

Firstpage

4458

Lastpage

4463

Abstract

Interpolation techniques are known to reduce computational complexity of model predictive control (MPC) (Bacic et al., 2003), (Rossiter et al., 2004). This paper presents the general interpolation based MPC (IMPC) for a constrained linear system with bounded disturbances. The resulting MPC control law comprises an interpolation between several single MPC control laws. Compared with single MPC control law implementations, the proposed approach has the advantage of combining the merits of having a large domain of attraction and good asymptotic behavior. The performances of the approach are presented via an example.

Keywords

computational complexity; interpolation; predictive control; Interpolation techniques; bounded disturbances; computational complexity; constrained linear system; interpolated model predictive controller; Cities and towns; Computational complexity; Computational efficiency; Computational modeling; Control system synthesis; Control systems; Interpolation; Linear systems; Predictive control; Predictive models; Interpolated model predictive control; Invariant set; Linear constrained systems with bounded disturbances;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2007. ACC '07

Conference_Location

New York, NY

ISSN

0743-1619

Print_ISBN

1-4244-0988-8

Electronic_ISBN

0743-1619

Type

conf

DOI

10.1109/ACC.2007.4282367

Filename

4282367