TY - JOUR
T1 - Network decomposition for distributed control through community detection in input–output bipartite graphs
AU - Tang, Wentao
AU - Daoutidis, Prodromos
PY - 2018/4
Y1 - 2018/4
N2 - This paper addresses the decomposition of network systems for distributed control. We construct a novel weighted input–output bipartite graph representation of control systems, in which the input–output edge weights capture topological connectivity and short-time response sensitivities. We then introduce community detection as a network-theoretic tool to generate a decomposition with strong intra-subsystem interactions and weak inter-subsystem interactions. A modularity-based graph bisection procedure is applied recursively to determine the optimal decomposition. The proposed method is applied to a chemical process network example.
AB - This paper addresses the decomposition of network systems for distributed control. We construct a novel weighted input–output bipartite graph representation of control systems, in which the input–output edge weights capture topological connectivity and short-time response sensitivities. We then introduce community detection as a network-theoretic tool to generate a decomposition with strong intra-subsystem interactions and weak inter-subsystem interactions. A modularity-based graph bisection procedure is applied recursively to determine the optimal decomposition. The proposed method is applied to a chemical process network example.
KW - Control architecture design
KW - Distributed control
KW - Network decomposition
UR - http://www.scopus.com/inward/record.url?scp=85044361360&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044361360&partnerID=8YFLogxK
U2 - 10.1016/j.jprocont.2018.01.009
DO - 10.1016/j.jprocont.2018.01.009
M3 - Article
AN - SCOPUS:85044361360
VL - 64
SP - 7
EP - 14
JO - Journal of Process Control
JF - Journal of Process Control
SN - 0959-1524
ER -