Paper Info
Title
Robust Model Predictive Control of Nonlinear Systems With Bounded and State-Dependent Uncertainties
Abstract
In this note, a robust model predictive control scheme for constrained discrete-time nonlinear systems affected by bounded disturbances and state-dependent uncertainties is presented. In order to guarantee the robust satisfaction of the state constraints, restricted constraint sets are introduced in the optimization problem, by exploiting the state-dependent nature of the considered class of uncertainties. Moreover, unlike the nominal model predictive control algorithm, a stabilizing state constraint is imposed at the end of the control horizon in place of the usual terminal constraint posed at the end of the prediction horizon. The regional input-to-state stability of the closed-loop system is analyzed. A simulation example shows the effectiveness of the proposed approach.
Year
DOI
Venue
2009
10.1109/TAC.2009.2020641
Automatic Control, IEEE Transactions
Keywords
Field
DocType
closed loop systems,discrete time systems,nonlinear control systems,optimisation,predictive control,robust control,uncertain systems,bounded disturbance,closed loop system,constraint set,discrete-time nonlinear system,input-to-state stability,optimization problem,prediction horizon,robust model predictive control,state-dependent uncertainty,Constrained systems,input-to-state stability,model predictive control,nonlinear discrete-time systems,robust control
Mathematical optimization,Linear system,Control theory,Nonlinear control,Model predictive control,Robustness (computer science),Robust control,Optimization problem,Mathematics,Bounded function,Constrained optimization
Journal
Volume
Issue
ISSN
54
7
0018-9286
Citations 
PageRank 
References 
26
1.09
12
Authors
4
Name
Order
Citations
PageRank
Gilberto Pin113617.21
D.M. Raimondo214013.33
Lalo Magni331830.63
T Parisini4935113.17