Robust model predictive control using saturated linear feedback controller

Author

Li, Zhijun ; Shi, Yuntao ; Yang, Yang ; Hao, Dongdong ; Sun, Dehui

Author_Institution

Key Lab. of Beijing for Field-bus Technol. & Autom., North China Univ. of Technol., Beijing

fYear

2008

fDate

1-3 Sept. 2008

Firstpage

665

Lastpage

671

Abstract

A robust model predictive control algorithm is proposed for linear systems with input saturation in this paper. Polytopic uncertain systems and structured time-varying uncertain systems are respectively considered. An infinite horizon worst-case performance function is used as on-line optimization objective. Using a saturated linear feedback controller, the control action can be obtained by solving an LMI optimization problem. The feasibility of the optimization problem at the original time implies the robust stability of the closed-loop control system. Two simulation examples are used to demonstrate the effectiveness of proposed algorithm.

Keywords

closed loop systems; control nonlinearities; feedback; linear matrix inequalities; linear systems; predictive control; robust control; time-varying systems; uncertain systems; LMI optimization problem; closed-loop control system; infinite horizon worst-case performance function; linear systems; polytopic uncertain systems; robust model predictive control; robust stability; saturated linear feedback controller; structured time-varying uncertain systems; Adaptive control; Infinite horizon; Linear feedback control systems; Linear systems; Prediction algorithms; Predictive control; Predictive models; Robust control; Time varying systems; Uncertain systems; input saturation; linear matrix inequality; model uncertainty; robust model predictive control;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on

Conference_Location

Qingdao

Print_ISBN

978-1-4244-2502-0

Electronic_ISBN

978-1-4244-2503-7

Type

conf

DOI

10.1109/ICAL.2008.4636233

Filename

4636233