Paper Info
Title
Robust Stability Properties Of Mpc Iteration Schemes Based On Relaxed Barrier Functions
Abstract
We analyze the robust stability properties of iteration schemes for the algorithmic implementation of model predictive control for linear discrete-time systems. The underlying optimization employs a relaxed barrier function based problem formulation and performs only a limited, possibly arbitrarily small, number of optimization algorithm iterations per sampling instant. Based on the input-to-state stability concept, the resulting overall closed-loop system consisting of system state and optimizer dynamics is shown to be robustly stable with respect to both external and internal disturbances. Implications for the case of certainty equivalence output feedback as well as possible extensions are also discussed.
Year
Venue
Field
2016
2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC)
Mathematical optimization,Certainty equivalence,Control theory,Computer science,Model predictive control,Exponential stability,Sampling (statistics),Optimization algorithm
DocType
ISSN
Citations 
Conference
0743-1546
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Feller, C.1152.90
Christian Ebenbauer220030.31