TY - GEN
T1 - Non-Linear Networked Systems Analysis and Synthesis using Dissipativity Theory
AU - Welikala, Shirantha
AU - Lin, Hai
AU - Antsaklis, Panos J.
N1 - Publisher Copyright:
© 2023 American Automatic Control Council.
PY - 2023
Y1 - 2023
N2 - We consider networked systems comprised of interconnected sets of non-linear subsystems and develop linear matrix inequality (LMI) techniques for their analysis and interconnection topology synthesis using only the dissipativity properties of the involved subsystems. In particular, we consider four networked system configurations (NSCs) and show that the analysis of their stability/dissipativity can be formulated as corresponding LMI problems. Using some matrix identities and mild assumptions, we also show that the synthesis of interconnection typologies for these NSCs can also be formulated as LMI problems. This enables synthesizing the interconnection topology among subsystems to enforce/optimize specific stability/dissipativity properties over the networked system. The formulated LMI problems can be solved efficiently and scalably using standard convex optimization toolboxes. We also provide several numerical examples to illustrate our theoretical results.
AB - We consider networked systems comprised of interconnected sets of non-linear subsystems and develop linear matrix inequality (LMI) techniques for their analysis and interconnection topology synthesis using only the dissipativity properties of the involved subsystems. In particular, we consider four networked system configurations (NSCs) and show that the analysis of their stability/dissipativity can be formulated as corresponding LMI problems. Using some matrix identities and mild assumptions, we also show that the synthesis of interconnection typologies for these NSCs can also be formulated as LMI problems. This enables synthesizing the interconnection topology among subsystems to enforce/optimize specific stability/dissipativity properties over the networked system. The formulated LMI problems can be solved efficiently and scalably using standard convex optimization toolboxes. We also provide several numerical examples to illustrate our theoretical results.
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U2 - 10.23919/ACC55779.2023.10155851
DO - 10.23919/ACC55779.2023.10155851
M3 - Conference contribution
AN - SCOPUS:85161087651
T3 - Proceedings of the American Control Conference
SP - 2951
EP - 2956
BT - 2023 American Control Conference, ACC 2023
T2 - 2023 American Control Conference, ACC 2023
Y2 - 31 May 2023 through 2 June 2023
ER -