Paper Info
Title
Scalable Identification Of Stable Positive Systems
Abstract
Positive systems frequently appear in applications, and enjoy substantially simplified analysis and control design compared to the general LTI case. In this paper we construct a polytopic parameterization of all stable positive systems, and a convex upper bound for simulation error (a.k.a. output error) for which the resulting optimization is a linear program. Previous work on analogous methods for both the positive and general LTI case result in semidefinite programs. We exploit the decomposability of the constraints in these linear programs to develop distributed solutions applicable to identification of large-scale networked systems.
Year
Venue
Field
2016
2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC)
Mathematical optimization,Linear system,Parametrization,Control theory,Computer science,Upper and lower bounds,Regular polygon,Minification,Linear programming,Positive systems,Scalability
DocType
ISSN
Citations 
Conference
0743-1546
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Jack Umenberger194.90
Ian R. Manchester236135.92